The document discusses structure-based drug design and molecular docking. It begins with introductions to drug design, drug targets, and structure-based drug design. It then describes molecular docking as a technique to predict how small molecules bind to protein targets by calculating binding affinities. The document outlines the docking process, including generating a protein's molecular surface, matching ligand and protein atoms to determine potential orientations, and scoring docked poses to identify favorable interactions. It also discusses using docking for virtual screening to identify potential drug leads from compound libraries.
Molecular docking by harendra ...power point presentationHarendra Bisht
Molecular docking is a computational method used to predict how a small molecule, like a drug, binds to a larger target molecule, like a protein. It works by fitting the structures of the two molecules together to find the highest affinity binding mode. The docking process involves defining the active site on the target protein, generating possible positions for the small molecule to bind, scoring the interactions between them, and identifying the best binding pose. Docking can help researchers design new drugs that effectively interact with protein targets.
Finding PDB files of molecules, locating binding sites, positioning ligand to a macromolecule, building grid and grid parameter file, performing molecular docking, and analysis of docking results by looking over various energy parameters and uses in drug discovery technology.
Molecular docking is a computational method that predicts the preferred orientation of one molecule to another when bound and forming a stable complex. It involves finding the best match between two molecules and can be used for drug design and development by predicting the binding affinity between potential drug candidates and their protein targets. Common molecular docking approaches include shape complementarity, which describes interacting molecules as complementary surfaces, and simulation methods, which simulate the actual docking process and calculate interaction energies between molecules. Popular molecular docking software includes AutoDock, FlexX, and GOLD.
This document discusses structure-based and ligand-based drug design approaches. Structure-based design uses the 3D structure of biological targets to dock potential drug molecules. Ligand-based design analyzes similar molecules that bind to the target to derive pharmacophore models or quantitative structure-activity relationships (QSAR) to predict new candidates. Specific structure-based methods covered include docking tools like AutoDock and CDOCKER, and accounting for protein and complex flexibility. Ligand-based methods discussed are QSAR techniques like Comparative Molecular Field Analysis (CoMSIA) and Field Analysis (CoMFA). In conclusion, computational approaches like these are valuable for drug discovery by facilitating the identification and testing of new ligand
This document discusses de novo drug design, which aims to design novel drug molecules from scratch computationally. It describes the basic steps of de novo design programs, including analyzing the target protein's active site, building molecules, and evaluating candidates. The key goals are to design molecules that fit the active site and form favorable interactions. Constraints like hydrogen bonding and hydrophobic regions are extracted from the target structure to guide molecule generation and scoring. The end goal is to produce new molecular scaffolds that can inspire medicinal chemistry efforts.
De novo drug design is a computer-assisted process that uses the 3D structure of a receptor target to design new drug molecules. It involves determining the structure of existing drug-target complexes, designing modifications to existing lead compounds, and generating new chemical classes of compounds. The goal is to design drugs with the correct shape and functional groups to properly fit and interact within the target's binding site in order to produce the desired pharmacological effect. However, de novo design is challenging and rarely produces ideal compounds on its own. Software tools aim to automate and speed up the process by fitting and linking together molecular fragments to fill interaction sites in the binding pocket.
This document discusses structure-based drug design. It begins by explaining that structure-based drug design relies on knowledge of the three-dimensional structure of biological targets, usually determined through methods like X-ray crystallography. The structure of the target is then used to design ligands that will bind to the target. The process involves identifying drug targets, determining the target's structure, performing computer-aided drug design to identify potential binding ligands, and building or modifying ligands to optimize binding to the target.
1. Structure-based drug design relies on knowledge of the 3D structure of the biological target obtained through methods like X-ray crystallography. Candidate drugs that are predicted to bind with high affinity and selectivity to the target can be designed.
2. Structure-based drug design can be divided into ligand-based drug design and receptor-based drug design. Receptor-based drug design involves building ligands that fit within the target's binding pocket through stepwise assembly of fragments.
3. De novo drug design uses the target's 3D structure to design new molecules without existing leads. It involves determining lead complexes' structures and modifying leads using molecular modeling tools.
Molecular docking by harendra ...power point presentationHarendra Bisht
Molecular docking is a computational method used to predict how a small molecule, like a drug, binds to a larger target molecule, like a protein. It works by fitting the structures of the two molecules together to find the highest affinity binding mode. The docking process involves defining the active site on the target protein, generating possible positions for the small molecule to bind, scoring the interactions between them, and identifying the best binding pose. Docking can help researchers design new drugs that effectively interact with protein targets.
Finding PDB files of molecules, locating binding sites, positioning ligand to a macromolecule, building grid and grid parameter file, performing molecular docking, and analysis of docking results by looking over various energy parameters and uses in drug discovery technology.
Molecular docking is a computational method that predicts the preferred orientation of one molecule to another when bound and forming a stable complex. It involves finding the best match between two molecules and can be used for drug design and development by predicting the binding affinity between potential drug candidates and their protein targets. Common molecular docking approaches include shape complementarity, which describes interacting molecules as complementary surfaces, and simulation methods, which simulate the actual docking process and calculate interaction energies between molecules. Popular molecular docking software includes AutoDock, FlexX, and GOLD.
This document discusses structure-based and ligand-based drug design approaches. Structure-based design uses the 3D structure of biological targets to dock potential drug molecules. Ligand-based design analyzes similar molecules that bind to the target to derive pharmacophore models or quantitative structure-activity relationships (QSAR) to predict new candidates. Specific structure-based methods covered include docking tools like AutoDock and CDOCKER, and accounting for protein and complex flexibility. Ligand-based methods discussed are QSAR techniques like Comparative Molecular Field Analysis (CoMSIA) and Field Analysis (CoMFA). In conclusion, computational approaches like these are valuable for drug discovery by facilitating the identification and testing of new ligand
This document discusses de novo drug design, which aims to design novel drug molecules from scratch computationally. It describes the basic steps of de novo design programs, including analyzing the target protein's active site, building molecules, and evaluating candidates. The key goals are to design molecules that fit the active site and form favorable interactions. Constraints like hydrogen bonding and hydrophobic regions are extracted from the target structure to guide molecule generation and scoring. The end goal is to produce new molecular scaffolds that can inspire medicinal chemistry efforts.
De novo drug design is a computer-assisted process that uses the 3D structure of a receptor target to design new drug molecules. It involves determining the structure of existing drug-target complexes, designing modifications to existing lead compounds, and generating new chemical classes of compounds. The goal is to design drugs with the correct shape and functional groups to properly fit and interact within the target's binding site in order to produce the desired pharmacological effect. However, de novo design is challenging and rarely produces ideal compounds on its own. Software tools aim to automate and speed up the process by fitting and linking together molecular fragments to fill interaction sites in the binding pocket.
This document discusses structure-based drug design. It begins by explaining that structure-based drug design relies on knowledge of the three-dimensional structure of biological targets, usually determined through methods like X-ray crystallography. The structure of the target is then used to design ligands that will bind to the target. The process involves identifying drug targets, determining the target's structure, performing computer-aided drug design to identify potential binding ligands, and building or modifying ligands to optimize binding to the target.
1. Structure-based drug design relies on knowledge of the 3D structure of the biological target obtained through methods like X-ray crystallography. Candidate drugs that are predicted to bind with high affinity and selectivity to the target can be designed.
2. Structure-based drug design can be divided into ligand-based drug design and receptor-based drug design. Receptor-based drug design involves building ligands that fit within the target's binding pocket through stepwise assembly of fragments.
3. De novo drug design uses the target's 3D structure to design new molecules without existing leads. It involves determining lead complexes' structures and modifying leads using molecular modeling tools.
Molecular modelling can help reduce the time and risks of drug development. It is applied to target structural characterization, developing focused libraries for hit discovery, and lead development and optimization. Fragment-based drug design is an important advance, where drug candidates are built inside the target's binding site using small molecule fragments to improve affinity from micromolar to millimolar to nanomolar levels. Molecular modelling supports medicinal chemistry decisions by providing structural insights into how drug candidates interact with their targets.
The document discusses 2D-QSAR (Quantitative Structure-Activity Relationship) analysis methods. It defines QSAR as mathematical relationships linking chemical structure and pharmacological activity. It describes several common 2D-QSAR methods including Hansch analysis, Free Wilson analysis, and various statistical methods. Cluster analysis is discussed as a way to group similar molecules and select a diverse subset for analysis. Molecular descriptors that encode structural, electronic, and topological properties are also introduced.
A drug is defined as any substance that causes a physiological change in the body when introduced through various routes of administration. Drug design is the process of discovering new drugs based on knowledge of a biological target. De novo drug design is a continuous process that uses the 3D structure of a receptor to design new molecules that can bind to and modulate the target. It involves determining the structures of lead targets and complexes and using molecular modeling tools to modify lead compounds. Various computational methods exist for de novo drug design, including growing, linking, lattice-based sampling and molecular dynamics-based approaches.
This document discusses de novo drug design, which involves using a target receptor's 3D structure to design new molecules that can interact with it, without relying on existing leads. The key steps of computer-based de novo design are generating primary constraints from the receptor, deriving interaction sites in the binding pocket, building up ligand structures using methods like growing and linking fragments, scoring the ligands to evaluate binding affinity, and applying secondary constraints related to drug properties. Successful applications of de novo design include HIV protease inhibitors and COMT inhibitors.
conformational search used in Pharmacophore mappingVishakha Giradkar
Conformational analysis is used in pharmacophore mapping to identify the ideal conformation of a molecule that is biologically active. There are several methods to perform the conformational search, including systematic search, distance geometry, and clique detection algorithms. The systematic search method systematically varies torsion angles to generate conformations, while distance geometry randomly samples conformations. Clique detection algorithms search for common inter-feature distances within active molecules. The conformation search space can be large due to many possible torsion angle combinations, so these methods aim to efficiently explore the low-energy conformational space.
SAR BY NMR (Structure Activity Relationship by Using NMR)SAKEEL AHMED
SAR by NMR is the Nuclear Magnetic Resonance (NMR) based method in which small organic molecules that bind to the proximal site are identified, optimized and finally linked together to produce high-affinity ligands.
It is called “SAR by NMR” because the structure-activity relationship (SAR) is obtained by the Nuclear Magnetic Resonance (NMR).
It is based on the fragments approaches to drug design.
With this technique, compounds with nanomolar affinity for a target protein can be rapidly discovered by tethering two ligands with micromolar affinities.
The method reduces the chemical synthesis and the time required for the discovery of high-affinity ligands and is particularly useful in target-directed drugs research.
Molecular modelling encompasses theoretical and computational methods used to model molecular behavior. It involves computational drug design, computational biology, and materials science. Virtual screening is a computational technique used in drug discovery to search small molecule libraries and identify structures most likely to bind to targets like drug receptors and enzymes. Virtual screening can dock small molecules into known protein structures and automatically evaluate large libraries to find potential drug candidates. It has advantages of being reliable, cost-effective, time-saving, and increasing success rates. Virtual screening methods include ligand-based, structure-based, and hybrid approaches.
In Silico methods for ADMET prediction of new moleculesMadhuraDatar
The document discusses the importance of predicting absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of new molecules in silico during drug design. It describes how ADMET prediction techniques have evolved since 1863 and helped advance drug development. Factors considered in developing ADMET prediction models include the model purpose, required prediction speed and accuracy. Common molecular descriptors used in these models are also discussed. The document outlines methods for predicting various ADMET properties like permeability, solubility, distribution and metabolism in silico. Recent tools for computational ADMET prediction are also mentioned.
Presentation on insilico drug design and virtual screeningJoon Jyoti Sahariah
This presentation discusses in silico drug design and virtual screening techniques. It defines in silico drug design as using computational software to perform drug design based on knowledge of a biological target. The presentation outlines two main types of in silico drug design: ligand-based, which uses known ligands to derive a pharmacophore when the receptor is unknown, and structure-based, which relies on knowledge of the 3D receptor structure. It also describes virtual screening techniques as automatically evaluating large compound libraries for likelihood of binding to a target protein using computer programs based on ligand or structure models.
The document discusses structure-based in-silico virtual screening protocols. It describes virtual screening as using computer methods to discover new ligands based on a target protein's biological structure. The main goal is to reduce the enormous chemical space of potential compounds to a manageable number with the highest likelihood of becoming drug candidates. Molecular docking is a key method, involving sampling potential ligand positions in the protein's binding site and scoring the interactions. The document also discusses force field, empirical, and knowledge-based scoring functions used to evaluate docking poses. Applications mentioned include designing Hsp90 inhibitors for cancer treatment and identifying novel BACE1 inhibitors.
Combanitorial approach for drug discoveryShwetA Kumari
Combinatorial chemistry is a new approach to drug discovery that involves synthesizing and testing large libraries of compounds in parallel rather than one by one. This allows for more rapid and cost-effective discovery of potential drug leads. There are two main challenges in drug discovery that combinatorial chemistry addresses: identifying a lead compound with the desired biological activity, and optimizing the lead compound. Solid phase synthesis and solution phase synthesis are two main combinatorial methods. Case studies demonstrate how combinatorial synthesis approaches have been used to develop inhibitors of influenza endonuclease, kinase inhibitors, and modulators of orexin receptors.
This document provides instructions for performing molecular docking simulations using Autodock 4. It discusses downloading and installing Autodock, preparing receptor and ligand files in pdbqt format, setting up a grid parameter file to define the docking grid, preparing an Autodock parameter file to define the docking parameters, running Autodock to perform the docking simulation, and analyzing the results to view and evaluate the predicted binding modes.
Computational drug design uses computer-aided drug design (CADD) approaches like structure-based drug design, ligand-based drug design, and protein-ligand docking to rationally design new drug candidates. These CADD approaches leverage information about protein and ligand structures to predict how well potential drug molecules may bind to their target without relying on experimental screening. Key techniques include pharmacophore modeling, virtual screening, and predicting the binding pose and affinity of ligands docked in the target protein's active site. Accurate preparation of the protein structure is important for successful structure-based drug design applications like protein-ligand docking.
This document discusses key drug-like properties that are important for drug discovery. It covers properties such as solubility, permeability, metabolic stability and how they impact pharmacokinetics and bioavailability. Modifying a molecule's structure can optimize these properties. For example, adding ionizable groups can increase solubility while reducing logP or molecular weight. Understanding how changes impact multiple properties is crucial for medicinal chemists to design drug candidates with balanced absorption and response profiles.
This document discusses de novo drug design, which involves designing novel drug structures based on the receptor structure without using existing ligands. It describes various algorithms and methods for de novo drug design, including outside-in and inside-out methods, active site analysis, whole molecule fitting, site point connection, fragment connection, sequential buildup, and random connection/disconnection methods. Each method has advantages and disadvantages for suggesting potential drug molecules.
Protein docking is used to check the structure, position and orientation of a protein when it interacts with small molecules like ligands. Protein receptor-ligand motifs fit together tightly, and are often referred to as a lock and key mechanism. There are both high specificity and induced fit within these interfaces with specificity increasing with rigidity. The foremost thing that we need to start with a docking search is the sequence of our protein of interest. (Halperin et al., 2002).
Protein-protein interactions occur between two proteins that are similar in size. The interface between the two molecules tends to be flatter and smoother than those in interfaces of these interactions do not have the ability to alter protein-ligand interactions. Protein-protein interactions are usually more rigid, the conformation in order to improve binding and ease movement. (Smith and Sternberg, 2002).
The process of drug development has revolved around a screening approach, as nobody knows which compound or approach could serve as a drug or therapy. Such almost blind screening approach is very time-consuming and laborious. The goal of structure-based drug design is to find chemical structures fitting in the binding pocket of the receptor. Based on the three-dimensional structure of the target protein, it can automatically build ligand molecules within the binding pocket and subsequently screen them (Weil et al., 2004).
A homology model of the housefly voltage-gated sodium channel was developed to predict the location of binding sites for the insecticides fenvalerate, a synthetic pyrethroid, and DDT, an early generation organochlorine. The model successfully addresses the state-dependent affinity of pyrethroid insecticides. (O’Reilly et al., 2006).
in silico drug design and virtual screening techniqueMO.SHAHANAWAZ
This document discusses in-silico drug design and virtual screening techniques. It describes two main types of in-silico drug design: ligand-based drug design which uses known ligands to derive a pharmacophore, and structure-based drug design which relies on the 3D structure of the biological target. Virtual screening is defined as computationally evaluating large libraries of compounds. There are two categories of virtual screening: ligand-based which compares candidate ligands to a pharmacophore model, and structure-based which docks candidates into the target's binding site. Examples of each type of virtual screening technique are provided.
This document discusses computer aided drug design (CADD) as a modern tool for drug discovery. It begins by explaining the history and definition of CADD, then outlines the various stages and techniques used, including ligand-based design, structure-based design, molecular docking, and de novo design. Examples of approved drugs discovered through CADD are also provided. The document aims to explain how CADD can help streamline drug discovery by saving time and costs compared to traditional methods.
Molecular descriptors are numerical values that characterize molecular properties and structures. They can represent physicochemical properties or values derived from algorithmic techniques applied to molecular structures. Descriptors vary in complexity and computational requirements. Some are based on experimental data while others are algorithmic constructs. Two-dimensional (2D) descriptors are calculated from 2D structures and include counts, physicochemical properties, and topological indices. Three-dimensional (3D) descriptors encode spatial relationships and include fragment screens and pharmacophore keys.
The document discusses structure-based drug design (SBDD). It first provides background on drug design and SBDD. It then describes some key aspects of SBDD, including using the 3D structure of the biological target obtained from techniques like X-ray crystallography and NMR spectroscopy. It also discusses ligand-based and receptor-based drug design approaches. The document then outlines the typical steps involved in SBDD, including target selection, ligand selection, target preparation, docking, evaluating results, and discusses some molecular docking techniques and scoring functions used to predict binding.
This document discusses structure based drug design. It describes how drug design uses knowledge of biological targets to find new medications. Structure based drug design uses information about the 3D structure of protein targets to design ligands that bind to them. The main methods described are ligand-based drug design through database searching, and receptor-based drug design which builds ligands for a receptor. Molecular docking is also discussed as a key technique to predict how ligands bind to protein targets and identify potential drug candidates.
Molecular modelling can help reduce the time and risks of drug development. It is applied to target structural characterization, developing focused libraries for hit discovery, and lead development and optimization. Fragment-based drug design is an important advance, where drug candidates are built inside the target's binding site using small molecule fragments to improve affinity from micromolar to millimolar to nanomolar levels. Molecular modelling supports medicinal chemistry decisions by providing structural insights into how drug candidates interact with their targets.
The document discusses 2D-QSAR (Quantitative Structure-Activity Relationship) analysis methods. It defines QSAR as mathematical relationships linking chemical structure and pharmacological activity. It describes several common 2D-QSAR methods including Hansch analysis, Free Wilson analysis, and various statistical methods. Cluster analysis is discussed as a way to group similar molecules and select a diverse subset for analysis. Molecular descriptors that encode structural, electronic, and topological properties are also introduced.
A drug is defined as any substance that causes a physiological change in the body when introduced through various routes of administration. Drug design is the process of discovering new drugs based on knowledge of a biological target. De novo drug design is a continuous process that uses the 3D structure of a receptor to design new molecules that can bind to and modulate the target. It involves determining the structures of lead targets and complexes and using molecular modeling tools to modify lead compounds. Various computational methods exist for de novo drug design, including growing, linking, lattice-based sampling and molecular dynamics-based approaches.
This document discusses de novo drug design, which involves using a target receptor's 3D structure to design new molecules that can interact with it, without relying on existing leads. The key steps of computer-based de novo design are generating primary constraints from the receptor, deriving interaction sites in the binding pocket, building up ligand structures using methods like growing and linking fragments, scoring the ligands to evaluate binding affinity, and applying secondary constraints related to drug properties. Successful applications of de novo design include HIV protease inhibitors and COMT inhibitors.
conformational search used in Pharmacophore mappingVishakha Giradkar
Conformational analysis is used in pharmacophore mapping to identify the ideal conformation of a molecule that is biologically active. There are several methods to perform the conformational search, including systematic search, distance geometry, and clique detection algorithms. The systematic search method systematically varies torsion angles to generate conformations, while distance geometry randomly samples conformations. Clique detection algorithms search for common inter-feature distances within active molecules. The conformation search space can be large due to many possible torsion angle combinations, so these methods aim to efficiently explore the low-energy conformational space.
SAR BY NMR (Structure Activity Relationship by Using NMR)SAKEEL AHMED
SAR by NMR is the Nuclear Magnetic Resonance (NMR) based method in which small organic molecules that bind to the proximal site are identified, optimized and finally linked together to produce high-affinity ligands.
It is called “SAR by NMR” because the structure-activity relationship (SAR) is obtained by the Nuclear Magnetic Resonance (NMR).
It is based on the fragments approaches to drug design.
With this technique, compounds with nanomolar affinity for a target protein can be rapidly discovered by tethering two ligands with micromolar affinities.
The method reduces the chemical synthesis and the time required for the discovery of high-affinity ligands and is particularly useful in target-directed drugs research.
Molecular modelling encompasses theoretical and computational methods used to model molecular behavior. It involves computational drug design, computational biology, and materials science. Virtual screening is a computational technique used in drug discovery to search small molecule libraries and identify structures most likely to bind to targets like drug receptors and enzymes. Virtual screening can dock small molecules into known protein structures and automatically evaluate large libraries to find potential drug candidates. It has advantages of being reliable, cost-effective, time-saving, and increasing success rates. Virtual screening methods include ligand-based, structure-based, and hybrid approaches.
In Silico methods for ADMET prediction of new moleculesMadhuraDatar
The document discusses the importance of predicting absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of new molecules in silico during drug design. It describes how ADMET prediction techniques have evolved since 1863 and helped advance drug development. Factors considered in developing ADMET prediction models include the model purpose, required prediction speed and accuracy. Common molecular descriptors used in these models are also discussed. The document outlines methods for predicting various ADMET properties like permeability, solubility, distribution and metabolism in silico. Recent tools for computational ADMET prediction are also mentioned.
Presentation on insilico drug design and virtual screeningJoon Jyoti Sahariah
This presentation discusses in silico drug design and virtual screening techniques. It defines in silico drug design as using computational software to perform drug design based on knowledge of a biological target. The presentation outlines two main types of in silico drug design: ligand-based, which uses known ligands to derive a pharmacophore when the receptor is unknown, and structure-based, which relies on knowledge of the 3D receptor structure. It also describes virtual screening techniques as automatically evaluating large compound libraries for likelihood of binding to a target protein using computer programs based on ligand or structure models.
The document discusses structure-based in-silico virtual screening protocols. It describes virtual screening as using computer methods to discover new ligands based on a target protein's biological structure. The main goal is to reduce the enormous chemical space of potential compounds to a manageable number with the highest likelihood of becoming drug candidates. Molecular docking is a key method, involving sampling potential ligand positions in the protein's binding site and scoring the interactions. The document also discusses force field, empirical, and knowledge-based scoring functions used to evaluate docking poses. Applications mentioned include designing Hsp90 inhibitors for cancer treatment and identifying novel BACE1 inhibitors.
Combanitorial approach for drug discoveryShwetA Kumari
Combinatorial chemistry is a new approach to drug discovery that involves synthesizing and testing large libraries of compounds in parallel rather than one by one. This allows for more rapid and cost-effective discovery of potential drug leads. There are two main challenges in drug discovery that combinatorial chemistry addresses: identifying a lead compound with the desired biological activity, and optimizing the lead compound. Solid phase synthesis and solution phase synthesis are two main combinatorial methods. Case studies demonstrate how combinatorial synthesis approaches have been used to develop inhibitors of influenza endonuclease, kinase inhibitors, and modulators of orexin receptors.
This document provides instructions for performing molecular docking simulations using Autodock 4. It discusses downloading and installing Autodock, preparing receptor and ligand files in pdbqt format, setting up a grid parameter file to define the docking grid, preparing an Autodock parameter file to define the docking parameters, running Autodock to perform the docking simulation, and analyzing the results to view and evaluate the predicted binding modes.
Computational drug design uses computer-aided drug design (CADD) approaches like structure-based drug design, ligand-based drug design, and protein-ligand docking to rationally design new drug candidates. These CADD approaches leverage information about protein and ligand structures to predict how well potential drug molecules may bind to their target without relying on experimental screening. Key techniques include pharmacophore modeling, virtual screening, and predicting the binding pose and affinity of ligands docked in the target protein's active site. Accurate preparation of the protein structure is important for successful structure-based drug design applications like protein-ligand docking.
This document discusses key drug-like properties that are important for drug discovery. It covers properties such as solubility, permeability, metabolic stability and how they impact pharmacokinetics and bioavailability. Modifying a molecule's structure can optimize these properties. For example, adding ionizable groups can increase solubility while reducing logP or molecular weight. Understanding how changes impact multiple properties is crucial for medicinal chemists to design drug candidates with balanced absorption and response profiles.
This document discusses de novo drug design, which involves designing novel drug structures based on the receptor structure without using existing ligands. It describes various algorithms and methods for de novo drug design, including outside-in and inside-out methods, active site analysis, whole molecule fitting, site point connection, fragment connection, sequential buildup, and random connection/disconnection methods. Each method has advantages and disadvantages for suggesting potential drug molecules.
Protein docking is used to check the structure, position and orientation of a protein when it interacts with small molecules like ligands. Protein receptor-ligand motifs fit together tightly, and are often referred to as a lock and key mechanism. There are both high specificity and induced fit within these interfaces with specificity increasing with rigidity. The foremost thing that we need to start with a docking search is the sequence of our protein of interest. (Halperin et al., 2002).
Protein-protein interactions occur between two proteins that are similar in size. The interface between the two molecules tends to be flatter and smoother than those in interfaces of these interactions do not have the ability to alter protein-ligand interactions. Protein-protein interactions are usually more rigid, the conformation in order to improve binding and ease movement. (Smith and Sternberg, 2002).
The process of drug development has revolved around a screening approach, as nobody knows which compound or approach could serve as a drug or therapy. Such almost blind screening approach is very time-consuming and laborious. The goal of structure-based drug design is to find chemical structures fitting in the binding pocket of the receptor. Based on the three-dimensional structure of the target protein, it can automatically build ligand molecules within the binding pocket and subsequently screen them (Weil et al., 2004).
A homology model of the housefly voltage-gated sodium channel was developed to predict the location of binding sites for the insecticides fenvalerate, a synthetic pyrethroid, and DDT, an early generation organochlorine. The model successfully addresses the state-dependent affinity of pyrethroid insecticides. (O’Reilly et al., 2006).
in silico drug design and virtual screening techniqueMO.SHAHANAWAZ
This document discusses in-silico drug design and virtual screening techniques. It describes two main types of in-silico drug design: ligand-based drug design which uses known ligands to derive a pharmacophore, and structure-based drug design which relies on the 3D structure of the biological target. Virtual screening is defined as computationally evaluating large libraries of compounds. There are two categories of virtual screening: ligand-based which compares candidate ligands to a pharmacophore model, and structure-based which docks candidates into the target's binding site. Examples of each type of virtual screening technique are provided.
This document discusses computer aided drug design (CADD) as a modern tool for drug discovery. It begins by explaining the history and definition of CADD, then outlines the various stages and techniques used, including ligand-based design, structure-based design, molecular docking, and de novo design. Examples of approved drugs discovered through CADD are also provided. The document aims to explain how CADD can help streamline drug discovery by saving time and costs compared to traditional methods.
Molecular descriptors are numerical values that characterize molecular properties and structures. They can represent physicochemical properties or values derived from algorithmic techniques applied to molecular structures. Descriptors vary in complexity and computational requirements. Some are based on experimental data while others are algorithmic constructs. Two-dimensional (2D) descriptors are calculated from 2D structures and include counts, physicochemical properties, and topological indices. Three-dimensional (3D) descriptors encode spatial relationships and include fragment screens and pharmacophore keys.
The document discusses structure-based drug design (SBDD). It first provides background on drug design and SBDD. It then describes some key aspects of SBDD, including using the 3D structure of the biological target obtained from techniques like X-ray crystallography and NMR spectroscopy. It also discusses ligand-based and receptor-based drug design approaches. The document then outlines the typical steps involved in SBDD, including target selection, ligand selection, target preparation, docking, evaluating results, and discusses some molecular docking techniques and scoring functions used to predict binding.
This document discusses structure based drug design. It describes how drug design uses knowledge of biological targets to find new medications. Structure based drug design uses information about the 3D structure of protein targets to design ligands that bind to them. The main methods described are ligand-based drug design through database searching, and receptor-based drug design which builds ligands for a receptor. Molecular docking is also discussed as a key technique to predict how ligands bind to protein targets and identify potential drug candidates.
(Kartik Tiwari) Denovo Drug Design.pptxKartik Tiwari
Hygia Institute of Pharmaceutical Education and Research provides information on drug design. There are two main types of drug design: ligand-based which relies on existing molecules that bind to the target, and structure-based which relies on the 3D structure of the target. De-novo drug design uses the 3D structure of the receptor to design new molecules and involves optimizing ligands to fit the receptor's active site properties. LUDI software aids de-novo design through identifying interaction sites in the receptor, fitting molecular fragments, and linking fragments together to form new drug candidates.
In spite of extensive effort by industry and academia to develop new drugs, there are still several diseases that are in need of therapeutic agents and have yet to be developed.
10 years the identification rate of disease-associated targets has been higher than the therapeutics identification rate.
Nevertheless, it is apparent that computational tools provide high hopes that many of the diseases under investigation can be brought under control.
Computer Added Drug Design is one of the latest technology of medicine world. This short slide will help you to know a little about CADD.If you want to know a vast plz go throw the reference book.
Hey students here i am attaching the powerpoint presenatation on the Receptor/enzyme-interaction and its analysis, Receptor/enzyme cavity size prediction, predicting
the functional components of cavities and the concept regarding the fragment based drug design.
Rational drug design is a process that begins with knowledge of a biological target through which new medications can be discovered. It involves designing small molecules that are complementary in shape and charge to bind to the target. The goal is to activate or inhibit the target's function in a way that provides a therapeutic benefit. In contrast to traditional trial-and-error testing, rational drug design starts with a hypothesis about how modulating a specific target could have therapeutic value. Computer modeling is often used to aid in drug design, and knowing the three-dimensional structure of the target enables structure-based drug design.
Computer aided drug design uses computational approaches to aid in the drug discovery process. There are several key approaches including ligand based approaches which identify characteristics of known active ligands, target based approaches which use information about the biological target, and structure based drug design which utilizes 3D structural information. The main steps in drug design include target identification and validation, lead identification and optimization, and preclinical and clinical trials. Computational tools are used throughout the process for tasks like molecular docking, ADMET prediction, and structure activity relationship analysis.
Computational chemistry is a branch of chemistry that uses computer simulation to assist in solving complex chemical problems. It exploits methods of theoretical chemistry, incorporated into efficient computer programs, to calculate the structures, the interactions, and the properties of molecules
In silico drug design/Molecular dockingKannan Iyanar
This document discusses rational drug design using computational methods. It begins by explaining how drugs work by binding to biological targets like proteins. It then discusses the need for new drugs to treat new diseases or improve current treatments. The document outlines several methods for screening and designing new drugs, including studying natural products, making modifications, and rational drug design based on understanding the molecular disease process. It describes using the 3D structure of protein targets and molecular docking to design ligands that selectively bind targets. The goals of drug design are to find molecules that effectively bind targets while also having suitable absorption, distribution, metabolism, excretion and toxicity properties. Computational methods can help streamline the drug discovery process.
This document provides a summary of a student assignment on drug design and toxicology. It discusses several topics:
1) It outlines the drug design process and different types of drug design approaches, including ligand-based and structure-based design.
2) It discusses the importance of toxicology testing in drug development to evaluate safety. Topics covered include emerging safety biomarkers, establishing human first-dose levels, pathway analysis, and genomic biomarker usage.
3) It explores various dynamic QSAR techniques and their applications in drug design and toxicology, as well as examining ADME and toxicology relationships.
Computer Assisted Drug Design By Rauf Pathan and Patel Mo ShaffanPathan Rauf Khan
CADD is modern technique of drug design and use of this technique reduce drug screening time and discover new drugs with specific therapeutic activity.
This document discusses molecular docking and its role in modern drug discovery. It begins with an introduction to docking and abbreviations used. It then explains that docking attempts to find the best match between two molecules and discusses how molecular docking is used in structure-based drug design. Applications of molecular docking mentioned include virtual screening to identify potential drug candidates from large libraries and optimizing ligands through studying their binding geometries with target proteins. The document concludes that molecular docking makes promising contributions to drug discovery by aiding in lead identification and optimization.
1) The document discusses the basics of drug design including defining the disease process, identifying targets for drug design like enzymes, receptors and nucleic acids, and the different approaches of ligand-based drug design and structure-based drug design.
2) It also covers important techniques in drug design like computer-aided drug design using computational methods, quantitative structure-activity relationships (QSAR), and the uses of computer graphics in molecular modeling and dynamics simulations.
3) Important experimental techniques discussed are x-ray crystallography and NMR spectroscopy that provide structural information for target biomolecules essential for structure-based drug design.
The document discusses lead identification and optimization in drug design. It describes the general drug discovery process which includes target validation, assay development, high-throughput screening, hit to lead identification, and lead optimization stages. Lead optimization is one of the most important steps and involves modifying lead compounds to improve potency, selectivity, and pharmacokinetic parameters. Structure-based and ligand-based drug design approaches are used, along with in silico tools to predict properties like toxicity and ensure drug-likeness. Key steps in structure-based design include identifying the binding site and growing fragments in an iterative process until an optimized lead is obtained.
Pharmacophore Modeling and Docking Techniques.pptDrVivekChauhan1
Pharmacophore modeling and molecular docking techniques are important computational methods used in drug design and discovery. Pharmacophore models identify the essential molecular features responsible for biological activity. Molecular docking predicts how drug molecules bind to protein targets. The document discusses key concepts like pharmacophores, bioisosterism, and molecular docking workflows. It also covers common docking software and factors that influence docking results like intramolecular forces and target preparation. Overall, the document provides an overview of pharmacophore modeling and molecular docking techniques that are widely applied in rational drug design.
This document provides an overview of rational drug design approaches. It discusses structure-based drug design which relies on knowledge of the target structure obtained through methods like X-ray crystallography. Homology modeling and docking are described as part of structure-based design. Ligand-based design relies on knowledge of other molecules that bind the target and uses techniques like pharmacophore modeling and quantitative structure-activity relationships. Key aspects of pharmacophore modeling, scaffold hopping, and de novo design are also summarized. The document provides a comprehensive yet concise introduction to rational drug design methods.
This document discusses rational drug design, which involves designing drugs based on knowledge of biological targets. It describes two main approaches: structure-based drug design, which relies on determining the 3D structure of the target using techniques like X-ray crystallography, and ligand-based drug design, which relies on knowledge of molecules that already bind to the target. Structure-based design involves identifying a drug target, determining its structure and function, then designing drugs that interact with it beneficially. Homology modeling can be used to model targets when experimental structures are unavailable. The document outlines the steps of structure-based design in rational drug development.
The techniques of drug designing and in silico studies are well defines in this presentation. Mooreover, the various softwares which are used in new era for determining the drug targets inside the body are elaborated.
This document provides an overview of various chromatography techniques. It defines chromatography as a physical separation method that distributes components between a stationary and mobile phase. The document then discusses the history of chromatography and provides examples of different types including thin layer chromatography, paper chromatography, and ion exchange chromatography. It explains the basic principles and components of thin layer chromatography and paper chromatography.
The document discusses water sampling procedures and techniques. It describes the process of collecting water samples from various sources for analysis. This includes grabbing discrete samples at specific times and locations or taking composite samples by mixing multiple grab samples. The document outlines sampling methods like systematic, random, judgmental and stratified. It also details equipment used for surface water sampling like buckets, scoops and specialized samplers, as well as groundwater sampling tools like bailers, suction lift pumps and submersible pumps. Proper procedures are emphasized to obtain representative samples and avoid contamination.
This document provides an overview of organometallic chemistry. It discusses the types of bonds that can occur in organometallic compounds, including s and p bonds between metals and carbon ligands. It also discusses the 18 electron rule for transition metal complexes and how this rule can be used to predict stability and reactivity. Synthesis of organometallic compounds is covered, including methods such as metathetical reactions and oxidative addition. Common ligands like alkyls and carbonyls are also described, including how their bonding interacts with the metal centers.
This document discusses various molecular biology concepts and techniques used in DNA and protein analysis. It defines key terms like DNA, RNA, genes, transcription, translation and genome. It also describes commonly used techniques like PCR, gel electrophoresis, Southern blotting and different types of genetic markers. The document provides details on the basic principles, applications and differences between techniques like RFLP, AFLP, RAPD analysis.
This document discusses organometallic chemistry and some of its applications in organic synthesis. Specifically, it outlines five fundamental organometallic reaction types: 1) Lewis acid dissociation, 2) Lewis base dissociation, 3) oxidative addition, 4) reductive elimination, and 5) insertion. It provides examples of each reaction type and notes how they can change the metal's oxidation state, electron count, and coordination number. The document also discusses how organometallic complexes can alter a molecule's reactivity through effects on functional groups, symmetry, and stability.
This document provides an overview of nuclear chemistry concepts including:
- Nuclides are characterized by atomic number and nucleon number. Neutrons increase nuclear stability through the strong force.
- Nuclear reactions involve changes to the nucleus, unlike chemical reactions which involve electrons. Different isotopes of the same element can undergo different nuclear reactions.
- Radioactive emissions ionize atoms and molecules, producing reactive particles that can damage body tissues, leading to immediate and delayed effects like cancer.
- Nuclear energy is released from binding energy changes during fusion of small atoms or fission of large atoms. A chain reaction in a nuclear reactor can produce controlled nuclear fission energy.
This document summarizes key concepts in nuclear chemistry including:
1) The discovery of radioactivity by scientists like Roentgen, Becquerel, and the Curies who observed emissions from uranium that could pass through matter and expose photographic plates.
2) Types of nuclear radiation including alpha, beta, gamma and their properties.
3) The concept of radioactive decay and half-life and how radioactive isotopes decay into more stable elements.
4) Nuclear reactions including fission which is used in nuclear power plants and weapons, and fusion which powers the sun.
This document discusses different types of nuclear radiation and radioactive decay. It begins by defining radioactivity and radiation, and describes three main types of radiation: alpha, beta, and gamma. It then covers five types of radioactive decay: alpha particle production, beta particle production, gamma production, positron production, and electron capture. For each type of decay, it provides the nuclear equation format and explains how the atomic and mass numbers change. The document also discusses bombardment reactions and provides examples of bombardment with different particles.
This document discusses air sampling methods and gas chromatography. It provides an overview of different air sampling techniques for particulate and gaseous pollutants including filtration, impingement, precipitation, absorption, adsorption and condensation. It also describes the basic components and process of gas chromatography, including carrier gases, columns, stationary phases, detectors and how it can be used for qualitative analysis. Gas chromatography is presented as a technique to separate and analyze mixtures using differences in volatility and polarity between components.
Research methodology ch-1 presentation.pptxJabir Hussain
This document discusses key concepts in research methodology. It defines research as a systematic, careful investigation to gain new knowledge. The objectives of research include gaining insights, describing characteristics, determining frequencies, and testing hypotheses. Research can be descriptive, analytical, applied, fundamental, quantitative, qualitative, conceptual, or empirical. Key criteria for good research include clearly defining the purpose, providing sufficient methodological details, using objective and appropriate designs and analyses, and drawing justified conclusions.
Prodrugs are compounds that are inactive but are metabolized in the body to produce an active drug. This document discusses the concept of prodrugs and their applications in drug delivery. It was written by Jabir Hussain, a lecturer of chemistry at the University of Education Lahore campus in D.G. Khan.
The document provides guidance on proper specimen collection and transport procedures. It discusses appropriate containers, labeling, storage, and handling of spillages. Specimen containers must be sealed securely and labeled with patient details. Storage should be in a refrigerator if delivery to the lab is delayed. Only clinically-indicated specimens should be collected to avoid inappropriate antibiotic prescribing. Proper collection methods are outlined for various sample types like urine, sputum, and feces.
Chemiluminescent immunoassay is a variation of enzyme immunoassay that detects small biological molecules. It works by using enzyme-labeled antibodies and antigens, where the enzyme converts a substrate into a reaction product that emits light instead of changing color. This light emission, or luminescence, indicates the presence and amount of the targeted antigen in a sample. Benefits of this technique include ultra-sensitivity to detect small amounts of molecules, a wider dynamic range, and a linear relationship between light intensity and concentration of the substance measured.
This document discusses the mevalonate and deoxyxylulose phosphate pathways that produce terpenoids and steroids. It explains that acetyl CoA is the precursor that leads to isopentenyl pyrophosphate (IPP), the universal precursor for isoprenoid synthesis. IPP is the basic C5 building block that is linked in a head-to-tail fashion to form isoprenoid chains. Prenyl transferases catalyze this addition of isoprene units. The document also outlines several key steps in the biosynthesis of compounds like monoterpenes, sesquiterpenes, diterpenes, triterpenes, sterols, and carotenoids.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
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This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
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Exploiting Artificial Intelligence for Empowering Researchers and Faculty, In...Dr. Vinod Kumar Kanvaria
Exploiting Artificial Intelligence for Empowering Researchers and Faculty,
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How to Make a Field Mandatory in Odoo 17Celine George
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Main Java[All of the Base Concepts}.docxadhitya5119
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2. INTRODUCTION TO DRUG AND DRUG DESIGN
The drug is most commonly an organic small
molecule that activates or inhibits the function of
a bio molecule such as a protein, which in turn
results in a therapeutic benefit to the patient.
Drug design, or rational drug design or simply
rational design, is the inventive process of
finding new medications based on the
knowledge of a biological target.
Drug design involves the design of small
molecules that are complementary in shape and
charge to the bio molecular target with which
they interact and therefore will bind to it.
2
3. INTRODUCTION TO DRUG AND DRUG DESIGN
Drug design frequently but not necessarily relies
on computer modeling techniques.
This type of modeling is often referred to as
computer aided drug design.
Finally, drug design that relies on the knowledge
of the three-dimensional structure of the bio
molecular target is known as structure-based
drug design.
The phrase “drug design” is to some extent a
misnomer.
A more accurate term is ligand design (i.e.,
design of a small molecule that will bind tightly to
its target).
3
4. BACKGROUND
Biomolecular target (proteins or nucleic acids) is a key molecule
involved in a particular metabolic or signaling pathway that is leading
to a specific disease condition or pathology or to the infectivity or
survival of a microbial pathogen.
In Some cases, small molecules will be designed to inhibit the target
function in the specific pathway (diseased state).
Small molecules (inhibitors or modulators) will be designed that are
complementary to the active site/allosteric site of target.
In some other cases, small molecules will be designed or developed
to enhance the normal pathway by promoting specific biomolecular
molecules in the normal pathways that may have been affected in the
diseased state. 4
5. BACKGROUND
Small molecules (drugs) can be designed so as not to affect any other
important “off-target” molecules or anti targets, since drug interactions
with off-target molecules may lead to undesirable side effects.
Sequence homology is often used to identify such risks.
Most commonly, drugs are organic small molecules produced through
chemical synthesis, but biopolymer-based drugs (also known as
biologics) produced through biological processes are becoming
increasingly more common.
5
6. INTRODUCTION TO SBDD
Structure-based drug design (or direct drug design) relies on
knowledge of the three dimensional structure of the biological target
obtained through methods such as x-ray crystallography or NMR
spectroscopy.
If an experimental structure of a target is not available, it may be
possible to create a homology model of the target based on the
experimental structure of a related protein.
Using the structure of the biological target, candidate drugs that are
predicted to bind with high affinity and selectivity to the target may be
designed using interactive graphics and the intuition of a medicinal
6
chemist.
8. INTRODUCTION TO SBDD
Structure-based design is one of the first techniques to be used in
drug design.
Structure based drug design that has helped in the discovery process
of new drugs.
In parallel, information about the structural dynamics and electronic
properties about ligands are obtained from calculations.
This has encouraged the rapid development of the structure based
drug design.
structure-based drug design can be divided roughly into two
categories.
1. Ligand based Drug Design Or Database Searching
2. Receptor based Drug Design
8
9. LIGAND BASED DRUG DESIGN
The first category is about “finding” ligands for a given receptor, which
is usually referred as database searching.
In this case, a large number of potential ligand molecules are
screened to find those fitting the binding pocket of the receptor.
This method is usually referred as ligand-based drug design.
The key advantage of database searching is that it saves synthetic
effort to obtain new lead compounds.
9
10. RECEPTOR BASED DRUG DESIGN
Another category of structure-based drug design methods is
about “building” ligands, which is usually referred as receptor-
based drug design.
In this case, ligand molecules are built up within the constraints
of the binding pocket by assembling small pieces in a stepwise
manner.
These pieces can be either individual atoms or molecular
fragments.
The key advantage of such a method is that novel structures,
not contained in any database, can be suggested.
10
11.
12. Structure-based Drug Design (SBDD)
Molecular Biology & Protein Chemistry
3D Structure Determination of Target
and Target-Ligand Complex
Modelling
StructureAnalysis
and Compound Design
Biological Testing
Synthesis of New Compounds
If promising
Drug Design Cycle
Natural ligand / Screening
Pre-Clinical 12
Studies
13. 13
Ligand database Target Protein
Molecular docking
Ligand docked into protein’s active site
Structure-based Drug Design (SBDD)
Pharmacokinetic and Pharmacodynamic optimization
14. DOCKING
• Docking refers to the ability to position a ligand in the active or
a designated site of a protein and calculate the specific
binding affinities.
• Docking algorithms can be used to find ligands and binding
conformations at a receptor site close to experimentally
determined structures.
• Docking algorithms are also used to identify multiple proteins
to which a small molecule can bind.
• Some of the docking programs are GOLD (Genetic
Optimization for Ligand Docking), AUTODOCK, LUDI, HEX
etc. 14
15. What is Docking?
•Docking attempts to find the “best” matching between two molecules
•It includes finding the Right Key for the Lock
•Given two biological molecules determine:
- Whether the two molecules “interact”
- If so, what is the orientation that maximizes the “interaction” while
min
15
imizing the total “energy” of the complex
Goal: To be able to search a database of molecular structures and
retrieve all molecules that can interact with the query structure
17. Generate molecular surface of protein
Cavities in the receptor are used to
define spheres (blue); the centres
are potential locations for ligand atoms.
Sphere centres are matched to ligand
atoms, to determine possible orientations
for the ligand. 104 orientations generated
How DOCK works…….
17
18. Virtual screening, to identify
potential lead compounds from a
large dataset
Known structures of organic compounds
Libraries of Virtual Compounds
Programs calculate affinity for protein
Narrow down to small number of
possiblities
Surface representation that efficiently
represents the docking surface and
identifies the regions of interest (cavities
and protrusions)
Surface matching that matches surfaces
to optimize a binding score
18
19. Pose prediction
19
• If we know exactly where and how
a known ligand binds...
– We can see which parts are
important for binding
– We can suggest changes to
improve affinity
– Avoid changes that will ‘clash’
with the protein
20. Introducing flexibility:
Whole molecule docking programs
• Monte Carlo methods (MC)
• Molecular Dynamics (MD)
• Simulated Annealing (SA)
• Genetic Algorithms (GA)
Available in packages:
Auto Dock (MC,GA,SA)
GOLD (GA)
Sybyl (MD)
Glide (Schrodinger)
20
22. IMPORTANCE
Molecular Docking
Prediction of the
binding affinity
(Scoring Function)
Identification of the
ligand’s
correct binding
geometry
(pose) in the binding
site
(Binding Mode)
Rational Design Of
Drugs
22
23. TYPES OF DOCKING
Rigid Docking (Lock and Key)
geometry of
In rigid
both the
docking, the internal
receptor and ligand are
treated as rigid.
Flexible Docking (Induced fit)
An enumeration on the rotations of
one of the molecules (usually smaller one) is
performed. Every rotation the energy is calculated;
later the most optimum pose is selected.
23
24. DOCKING CAN BE BETWEEN….
Protein - Ligand
Protein – Protein
Protein – Nucleotide
24
27. TYPES OF INTERACTIONS
Electrostatic forces - Forces with electrostatic origin are due to the charges residing in
the matter.
Electrodynamics forces - The most widely known is probably the van der Waals
interaction.
Steric forces - These are caused by entropy. For example, in cases where entropy is
limited, there may be forces to minimize the free energy of the system.
Solvent-related forces – These are due to the structural changes of the solvent. These
structural changes are generated, when ions, colloids, proteins etc, are added into the
structure of solvent. The most commonly are Hydrogen bond and hydrophobic
interactions
27
28. A TYPICAL DOCKING WORKFLOW
T
ARGET
SELECTION
LIGAND
SELECTION
T
ARGET
PREP
ARA
TION
EV
ALUA
TING
DOCKINGRESUL
T
DOCKING
LIGAND
PREP
ARA
TION
28
29. Receptor selection and
preparation
should be
biologically
Building the Receptor
The 3D structure of the receptor should
be considered which can be downloaded
fromPDB.
The available
structure
processed.
The receptor should be
activeandstable.
Identification of the Active Site
The active site within the receptor
shouldbeidentified.
The receptor may have many active
sites but the one of the interest should be
selected.
Ligand selection and
preparation
Ligands can be obtained
various
from
databases like ZINC,
PubChem or can be sketched
using tools like Chemsketch.
Docking
The ligand is docked onto the
receptor and the interactions are
checked.
The scoring function generates
score, depending on which the
best fit ligand is selected.
KEY STAGES IN DOCKING
29
30. Why is docking important?
30
• It is the key to rational drug design: The results of docking can be used
to find inhibitors for specific target proteins and thus to design new drugs.
• It is gaining importance as the number of proteins whose structure is
known increases
• In addition to new drug discovery, it is of extreme relevance in cellular
biology, where function is accomplished by proteins interacting with
themselves and with other molecular components
31. USES OF DOCKING
Drug targets
Protein- ligand interactions that otherwise may be overlooked
Better understand the Machinery of Life
Enzyme-inhibitor class
Antibody-antigen class
Others
Protein Therapies
Engineered Protein Enzymes
Although the reliability of docking methods is not so high, they can
provide new suggestions
False positives rates can be reduced using several scoring functions
in a consensus-scoring strategy
31
32. APPLICATIONS
Virtual screening (hit identification)
Docking with a scoring function can be used to quickly screen large
databases of potential drugs in silico to identify molecules that are likely
to bind to protein target of interest.
Drug Discovery (lead optimization)
Docking can be used to predict in where and in which
orientation a ligand binds to a protein (binding mode or pose).
relative
This information may in turn be used to design more potent and
selective analogs.
Bioremediation
Protein ligand docking can also be used to predict pollutants that c3
a2
n
be degraded by enzymes.
33. FUTURE CHALLENGES FOR DOCKING
• Better Scoring Functions
• High-Throughput Screening
• Tractable Models of Flexibility
• The so-called computational molecular docking problem is far from
being solved. There are two major bottle-necks:
1. The algorithms can handle only a limited extent of
backbone flexibility
2. The availability of selective and efficient scoring functions
33
34. Receptor Ligand Approach Comments
known known DOCK receptor
based
Programmes-
AUTO-DOCK
known unknown De novo based GROW, LEGEND
unknown known Ligand based QSAR
unknown unknown Combinational
based
34
Different approaches based on structural availibility
35. DE NOVO APPROACHES
• De novo design is the approach to build a customized
Ligand for a given receptor.
• This approach involves the ligand optimization.
• Ligand optimization can be done by analyzing protein
active site properties that could be probable area of
contact by the ligand.
• The analyzed active site properties are described to
negative image of protein such as hydrogen bond,
hydrogen bond acceptor and hydrophobic contact region.35
36. DE NOVO DRUG DESIGN
De novo means start afresh, from the beginning, from
the scratch
It is a process in which the 3D structure of receptor is used
to design newer molecules
It involves structural determination of the lead target
complexes and lead modifications using molecular
modeling tools.
Information available about target receptor but no existing
leads that can interact. 36
37. PRINCIPLES OF DENOVO DRUG DESIGN
•Assembling possible compounds and evaluating their
quality.
•Searching the sample space for novel
drug like properties.
structures with
Protein Structure Build a model for Protein Structure
37
38. DENOVO DRUG DESIGN
•In de novo design, the structure of the target should be known to
a high resolution, and the binding to site must be well defined.
• This should defines
hypothetical interaction
not only a shape
sites, typically
constraint but
consisting of
hydrogen bonds, electrostatic
interactions.
and other non-covalent
• These can greatly reducing the sample space, as hydrogen
bonds and other anisotropic interactions can define specific
orientations. 38
39. DERIVATION OF INTERACTION SITES
39
A key step to model the binding site as accurately as possible.
• This starts with an atomic resolution structure of the active
site.
•Programs likeUCSF , DOCK define the volume available
to a ligand by filling the active site with spheres.
• Further constraints follow, using positions of H-bond
acceptors and donors.
•Other docking algorithms,
FlexiDock 16 use an all-atom
detail.
such as FLOG, GOLD, and
representations to achieve fine
•Ray-tracing algorithms, such as SMART
,represent another
strategy
41. .
Fragments are added to provide suitable interactions to both key sites and
space between key sites
These include simple hydrocarbon chains, amines, alcohols, and even
single rings.
In the case of multiple seeds, growth is usually simultaneous and
continues until all pieces have been integrated into a single molecule.
A Single Key Building Block is the starting point or Seed
41
45. LINKING
The fragments, atoms, or building blocks are either
placed at key interaction sites.
They are joined together using pre-defined rules to
yield a complete molecule.
Linking groups or linkers may be predefined or
generated to satisfy all required conditions . 45
46. Lattice based method
The lattice is placed in the binding site, and atoms around key
interaction sites are joined using the shortest path.
Then various iterations, each of which includes translation, rotation or
mutation of atoms, are guided by a potential energy function, eventually
leading to a target molecule.
46
48. Molecular Dynamics Methods
The building blocks are initially randomly placed and then
by MD simulations allowed to rearrange.
After each rearrangement certain bonds were broken and
the process repeated.
During this procedure high scoring structures were stored for
later evaluation.
48
49. SCORING
49
• Each solution should be tested to decide which is the most promising.
This is called as scoring.
•Programs such as LEGEND18, LUDI19, Leap-Frog16, SPROUT20,
HOOK21, and PRO-LIGAND22 attempt this using different scoring
techniques
•These scoring functions vary from simple steric constraints and
H-bond placement to explicit force fields and empirical or knowledge-
based scoring methods.
50. free energy by substituting the exact physical model with
simplified statistical methods.
•
SCORING-(CONT..)
• Programs like GRID and LigBuilder3 set up a grid in
the binding site and then assess interaction energies by
placing probe atoms or fragments at each grid point.
•Scoring functions guide the growth and optimization of
structures by assigning fitness values to the sampled
space
Scoring functions attempt to approximate the binding
50
51. Force fields usually involve more computation than the other types of
scoring functions eg:- LEGEND
• Empirical scoring functions are a weighted sum of individual ligand–
receptor interactions.
• Apart from scoring functions, attempts have been made to use NMR,
X-ray analysis and MS to validate the fragments
SCORING-(CONT..)
51
52. METHOD PROGRAMS AVAILABLE
Site point connection method LUDI
Fragment connection method SPLICE, NEW LEAD,
PRO-LIGAND
Sequential build up methods LEGEND, GROW, SPORUT
Random connection and
disconnection methods
CONCEPTS, CONCERTS, MCDNLG
52
53. DE NOVA DESIGN OF INHIBITOR FOR HIV-I PROTEASE
INHIBITOR
An impressive example of the application of SBDD was the design of
the HIV-I protease Inhibitor.
The starting point is the series of X-ray structures of the enzyme and
enzyme-inhibitor complex. The enzyme is made up of two equal
halves.
HIV protease is a symmetrical molecule with 2 equal halves and an
active site near its center like butterfly.
53
54. DE NOVA DESIGN OF INHIBITOR FOR HIV-I
PROTEASE INHIBITOR
For most such symmetrical molecules
with two equal halves and an active
site near its center like butterfly
For most
molecules,both
such symmetrical
halves have a
“business area”,or active site,that
carries out the enzymes job.
But HIV protease has only one such
active site in the center of the molecule
where the two halves meet.
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55. HIV 1 PROTEASE INHIBITOR
Structure of enzyme
Enzyme with inhibitor
55
58. EXAMPLES OF DRUGS DESIGNED BY STRUCTURE-
BASED METHODS
INHIBITOR TARGET DISEASE
HUMAN RENIN
ANTI HYPERTENSION
COLLAGENASE AND
STROMELYSIN
ANTICANCER ,ANTIARTHRITIS
PURINE NUCLEOTIDE
PHOSPHORYLASE
ANTIDEPRESSANT
THYMIDYLATE SYNTHASE ANTIPROLIFERATION
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59. Although a relatively new design method, de novo design
will play an ever-increasing role in modern drug design.
Though yet not able to automatically generate viable drugs
by itself, it is able to give rise to novel and often
unexpected drugs
when coupled with HTS, is proving to reduce drug
design turn around time.
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60. IMPROVE QUALITY OF LIFE
The emphasis now is not just on finding new ways to treat
human disease, but also on improving the quality of life of
people in general.
60