Lecture 1 –Introduction to drug design and development
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This document provides an overview of a lecture on drug design and development. It discusses four units that will be covered: drug design and development, drug targeting and identification, pharmacophore modeling, and computer aided drug design. It provides definitions of key terms like drug and pharmacology. It also summarizes the stages of new drug synthesis: drug discovery, pre-clinical development, and clinical development. Properties of an ideal drug and the role of bioinformatics in drug discovery are also briefly outlined.
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Pharmacophore modeling identifies key molecular features necessary for drug-target binding and biological response. It represents molecules schematically in 2D or 3D. Pharmacophore features include hydrogen bond donors/acceptors, aromaticity, hydrophobicity and hydrophilicity. Pharmacophore models are used for virtual screening to identify molecules that may activate or inhibit a target. There are two main types: ligand-based models extract common features of known ligands, while structure-based models define features from protein-ligand complex structures. Both aim to encode the optimal 3D arrangement of interactions between ligands and targets.
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QSAR (Quantitative Structure Activity Relationship) is a computational method that correlates chemical structures or properties of compounds to their biological activity. It transforms chemical structures into numerical descriptors for properties like solubility or log P (partition coefficient). QSAR models then use these descriptors in mathematical formulas to quantitatively predict biological activity based on structural or physicochemical properties. Key parameters used in QSAR include log P (measure of lipophilicity), Hammett constants (measure electron donating/withdrawing ability), and Taft steric parameters (measure steric effects on size and shape). Hansch analysis and linear free energy models are commonly used methods to perform regression analysis of biological activity data and generate predictive QSAR models.
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QSAR attempts to correlate biological activity to measurable physicochemical properties through mathematical equations. It relates parameters like lipophilicity (log P), electronic effects (Hammett constants), and steric effects to biological response. Various QSAR methods exist, including Hansch analysis which uses these substituent constants in its equations. More advanced techniques like CoMFA analyze fields around aligned molecules to model activity landscapes and identify favorable regions for activity. QSAR provides a framework for drug design and predicting activities of untested compounds.
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Computer aided drug design
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Structure base drug design
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QSAR (Quantitative Structure Activity Relationship) is a computational method that correlates chemical structures or properties of compounds to their biological activity. It transforms chemical structures into numerical descriptors for properties like solubility or log P (partition coefficient). QSAR models then use these descriptors in mathematical formulas to quantitatively predict biological activity based on structural or physicochemical properties. Key parameters used in QSAR include log P (measure of lipophilicity), Hammett constants (measure electron donating/withdrawing ability), and Taft steric parameters (measure steric effects on size and shape). Hansch analysis and linear free energy models are commonly used methods to perform regression analysis of biological activity data and generate predictive QSAR models.
Qsar
QSAR attempts to correlate biological activity to measurable physicochemical properties through mathematical equations. It relates parameters like lipophilicity (log P), electronic effects (Hammett constants), and steric effects to biological response. Various QSAR methods exist, including Hansch analysis which uses these substituent constants in its equations. More advanced techniques like CoMFA analyze fields around aligned molecules to model activity landscapes and identify favorable regions for activity. QSAR provides a framework for drug design and predicting activities of untested compounds.
Pharmacophore modelling and docking techniques.pptx
Pharmacophore modelling is used to identify the essential molecular features necessary for a ligand to bind to a biological target. A pharmacophore model can explain how structurally diverse ligands can bind to a common receptor site and can be used to design or discover new ligands that bind to the same receptor. Docking techniques are also used to model molecular recognition between ligands and biological targets. Together, pharmacophore modelling and docking can provide insights into ligand-receptor interactions and guide drug design and discovery efforts.
Computer aided drug design
This document provides an overview of the history and methods of drug discovery, including traditional and computer-aided approaches. It discusses the traditional drug discovery life cycle from hit identification through random screening and the use of natural products and synthetic chemicals. It then introduces computer-aided drug design (CADD) and describes how it can be used throughout the drug discovery process, including structure-based design, ligand-based design, and de novo design to speed up screening and enable more rational drug design. It also lists some advantages of CADD over traditional methods and examples of drugs successfully developed using these approaches.
Structure base drug design
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The document discusses various approaches used in drug design, including quantitative structure activity relationship (QSAR) analysis. QSAR uses physicochemical parameters like partition coefficient, electronic parameters, and steric parameters to develop mathematical models correlating a drug molecule's structure to its biological activity. The goal is to predict activity for new compounds and guide drug design. Parameters commonly used in QSAR include log P for hydrophobicity, Hammett constants for electronics, and Taft constants for sterics. Methods involve Hansch analysis, Free Wilson models, and other statistical techniques.
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Quantitative Structure Activity Relationship (QSAR)
SAR versus QSAR, History and development of QSAR, Types of physicochemical
parameters, experimental and theoretical approaches for the determination of
physicochemical parameters such as Partition coefficient, Hammet’s substituent
constant and Taft’s steric constant. Hansch analysis, Free Wilson analysis, 3D-QSAR
approaches like COMFA and COMSIA.
Stages of drug discovery
The document outlines the key stages of drug discovery:
1. Target identification involves finding the specific biomolecule target of a drug.
2. Target validation helps evaluate the potential of a target through assays and screening to find initial hits.
3. Lead discovery uses screening methods like molecular modeling or combinational chemistry to identify initial compounds ("leads") that interact with the target.
4. Lead optimization chemically modifies the leads to improve efficacy, safety and other drug properties.
5. Pre-clinical safety involves animal testing, toxicity studies and formulation before clinical trials in humans.
Target identification in drug discovery
The document discusses target identification in drug discovery. It begins by defining a target and explaining that target identification is the first step in drug discovery. It then discusses various approaches to target identification, including direct biochemical methods, genetic interaction methods, and computational inference methods. The document also discusses characteristics of drug targets and how drugs interact with targets at the molecular level. It provides examples of tools that can be used for target identification and validation, such as microarrays, antisense technology, and proteomics. In summary, the document outlines the process of target identification in drug discovery and various methods that can be used to identify and validate potential drug targets.
Pharmacophore modeling and docking techniques
Pharmacophore modeling and docking techniques are important in rational drug design. Pharmacophore modeling identifies common molecular features necessary for biological activity. It represents features as 3D structures, 2D substituents, or properties. Pharmacophore modeling is used in structure-based drug design through target identification and ligand-based design via virtual screening. Docking predicts ligand binding orientation to form stable complexes. It identifies correct poses and binding affinities to aid hit identification and drug discovery. Popular docking software includes AutoDock, GOLD, Hex and SANJEEVINI.
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Molecular docking is a method for predicting how two molecules, such as a ligand and its protein target, will interact and fit together in three dimensions. Docking has become an important tool in drug discovery for identifying potential binding conformations between drug candidates and protein targets. The key steps in a typical docking workflow involve selecting the receptor and ligand molecules, then using software to computationally predict the orientation of binding and evaluate the fit through scoring functions. Popular molecular docking software packages include AutoDock, GOLD, and Glide. Applications of docking include virtual screening in drug discovery and lead optimization.
Pharmacophore identification
The document discusses several key concepts in pharmacophore modeling:
1) A pharmacophore defines the important chemical features shared among active molecules, such as hydrogen bond donors/acceptors and hydrophobic regions.
2) Bioisosteres are atoms or groups with similar physical/chemical properties that produce similar biological effects.
3) 3D pharmacophores specify the spatial relationships between features as distance ranges and angles.
4) Constrained systematic searching and ensemble distance geometry are used to identify pharmacophores from a set of molecules while considering multiple conformations.
5) Clique detection identifies all possible combinations of pharmacophoric groups in molecules by finding "maximal completely connected subgraphs".
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THE DRUG DESIGN AND DEVELOPMENT BASED ON DRUG DISCOVERY ,HERE ITS NEED RATIONALE ARE EXPLAINED ALSO QSAR, MOLECULAR DOCKING ITS HISTORY NEED, STRUCTURE BASED DRUG DESIGN IN EASY WAY WE HAVE MENTIONED. THIS WILL MAKE READERS EASY TO COLLECT DATA AT A PLACE ALL OVER THIS IS FOR PHARMA STUDENTS, ACADEMICS, PROFESSIONL AND OST USEFUL FOR RESEARCHERS.
THANK YOU
HOPE YOU WILL LIKE AND SHARE
Structure Based Drug Design
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.
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The document discusses lead identification in drug development. It defines a lead compound as one that shows desired pharmaceutical activity and could potentially be developed into a drug. The document outlines the content to be presented, including an introduction to lead identification, what a lead is, properties of leads, and methods for identifying leads. Key methods discussed are random screening, non-random screening, high-throughput screening, and structure-based drug design.
Docking techniques
Docking is a computational method that predicts the preferred orientation of one molecule to another when bound to form a stable complex. It involves searching high-dimensional spaces to find the best matching between two molecules, such as a protein and ligand. Successful docking methods effectively search these spaces and use scoring functions to correctly rank candidate dockings in order to identify the correct ligand binding position and predict binding affinity.
Combanitorial approach for drug discovery
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This document discusses various applications of quantitative structure-activity relationships (QSAR) in drug research and development. It describes how QSAR can provide information on receptor sites from enzyme inhibition studies, help understand the importance of physicochemical properties like log P values, and enable the use of bioisosterism to modify compounds. QSAR has correctly predicted drug activity and toxicity and is useful in drug design before compounds are synthesized.
Computer Aided 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.
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• Role of Nucleic acid microarrays, Protein microarrays, Antisense
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Drug discovery begins with identifying a biological target associated with a disease. Targets are validated through techniques like gene silencing to confirm their role in the disease process. Potential drug candidates, or leads, are identified through screening libraries of compounds or rational drug design. Leads undergo optimization to improve their safety, efficacy, and other properties. The entire drug discovery and development process takes an average of 15 years and over $800 million, with high failure rates contributing to the rising costs of drug development.
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The document discusses various approaches used in drug design, including quantitative structure activity relationship (QSAR) analysis. QSAR uses physicochemical parameters like partition coefficient, electronic parameters, and steric parameters to develop mathematical models correlating a drug molecule's structure to its biological activity. The goal is to predict activity for new compounds and guide drug design. Parameters commonly used in QSAR include log P for hydrophobicity, Hammett constants for electronics, and Taft constants for sterics. Methods involve Hansch analysis, Free Wilson models, and other statistical techniques.
Drug Designing
The document discusses the process of drug design and development. It begins by defining drugs and their targets at the molecular level. Historically, drugs came from plants and natural products, but now they can be designed rationally through understanding disease processes. The drug design process involves identifying a target, discovering leads, and optimizing candidates through computer modeling and testing before clinical trials. Modern techniques like molecular modeling, virtual screening, and computer-aided design have made drug discovery more efficient, but it remains a long, complex, and expensive process.
Quantitative Structure Activity Relationship (QSAR)
SAR versus QSAR, History and development of QSAR, Types of physicochemical
parameters, experimental and theoretical approaches for the determination of
physicochemical parameters such as Partition coefficient, Hammet’s substituent
constant and Taft’s steric constant. Hansch analysis, Free Wilson analysis, 3D-QSAR
approaches like COMFA and COMSIA.
Stages of drug discovery
The document outlines the key stages of drug discovery:
1. Target identification involves finding the specific biomolecule target of a drug.
2. Target validation helps evaluate the potential of a target through assays and screening to find initial hits.
3. Lead discovery uses screening methods like molecular modeling or combinational chemistry to identify initial compounds ("leads") that interact with the target.
4. Lead optimization chemically modifies the leads to improve efficacy, safety and other drug properties.
5. Pre-clinical safety involves animal testing, toxicity studies and formulation before clinical trials in humans.
Target identification in drug discovery
The document discusses target identification in drug discovery. It begins by defining a target and explaining that target identification is the first step in drug discovery. It then discusses various approaches to target identification, including direct biochemical methods, genetic interaction methods, and computational inference methods. The document also discusses characteristics of drug targets and how drugs interact with targets at the molecular level. It provides examples of tools that can be used for target identification and validation, such as microarrays, antisense technology, and proteomics. In summary, the document outlines the process of target identification in drug discovery and various methods that can be used to identify and validate potential drug targets.
Pharmacophore modeling and docking techniques
Pharmacophore modeling and docking techniques are important in rational drug design. Pharmacophore modeling identifies common molecular features necessary for biological activity. It represents features as 3D structures, 2D substituents, or properties. Pharmacophore modeling is used in structure-based drug design through target identification and ligand-based design via virtual screening. Docking predicts ligand binding orientation to form stable complexes. It identifies correct poses and binding affinities to aid hit identification and drug discovery. Popular docking software includes AutoDock, GOLD, Hex and SANJEEVINI.
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The document discusses several key concepts in pharmacophore modeling:
1) A pharmacophore defines the important chemical features shared among active molecules, such as hydrogen bond donors/acceptors and hydrophobic regions.
2) Bioisosteres are atoms or groups with similar physical/chemical properties that produce similar biological effects.
3) 3D pharmacophores specify the spatial relationships between features as distance ranges and angles.
4) Constrained systematic searching and ensemble distance geometry are used to identify pharmacophores from a set of molecules while considering multiple conformations.
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THE DRUG DESIGN AND DEVELOPMENT BASED ON DRUG DISCOVERY ,HERE ITS NEED RATIONALE ARE EXPLAINED ALSO QSAR, MOLECULAR DOCKING ITS HISTORY NEED, STRUCTURE BASED DRUG DESIGN IN EASY WAY WE HAVE MENTIONED. THIS WILL MAKE READERS EASY TO COLLECT DATA AT A PLACE ALL OVER THIS IS FOR PHARMA STUDENTS, ACADEMICS, PROFESSIONL AND OST USEFUL FOR RESEARCHERS.
THANK YOU
HOPE YOU WILL LIKE AND SHARE
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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.
Lead identification
The document discusses lead identification in drug development. It defines a lead compound as one that shows desired pharmaceutical activity and could potentially be developed into a drug. The document outlines the content to be presented, including an introduction to lead identification, what a lead is, properties of leads, and methods for identifying leads. Key methods discussed are random screening, non-random screening, high-throughput screening, and structure-based drug design.
Docking techniques
Docking is a computational method that predicts the preferred orientation of one molecule to another when bound to form a stable complex. It involves searching high-dimensional spaces to find the best matching between two molecules, such as a protein and ligand. Successful docking methods effectively search these spaces and use scoring functions to correctly rank candidate dockings in order to identify the correct ligand binding position and predict binding affinity.
Combanitorial approach for drug discovery
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.
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This document discusses the key principles and processes involved in drug discovery and drug-receptor interactions. It outlines the steps of choosing a disease target, identifying a bioassay to test potential drug candidates, finding and isolating lead compounds, determining a drug's structure and effects, and identifying forces that cause drug-receptor binding such as covalent bonding, hydrogen bonding, and hydrophobic interactions. The goal is to discover and develop safe and effective therapeutic drugs through a scientific process.
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This document discusses various applications of quantitative structure-activity relationships (QSAR) in drug research and development. It describes how QSAR can provide information on receptor sites from enzyme inhibition studies, help understand the importance of physicochemical properties like log P values, and enable the use of bioisosterism to modify compounds. QSAR has correctly predicted drug activity and toxicity and is useful in drug design before compounds are synthesized.
Computer Aided 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.
1. An Overview of Drug Discovery Process.pdf
Target identification, target validation, lead identification and lead
Optimization.
• Economics of drug discovery.
• Target Discovery and validation-Role of Genomics, Proteomics and
Bioinformatics.
• Role of Nucleic acid microarrays, Protein microarrays, Antisense
technologies, siRNAs, antisense oligonucleotides, Zinc finger proteins.
• Role of transgenic animals in target validation.
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Pharmacology is the study of drugs and their interaction with living systems. The drug discovery process involves identifying biological targets, screening compounds against those targets, and optimizing lead candidates for further development. This includes preclinical research on animals to test toxicity, pharmacodynamics, and pharmacokinetics. Successful candidates then progress to clinical trials in human subjects in four phases before the drug can be approved, manufactured, and marketed. The entire process from discovery to approval takes an average of 10-12 years and over $500 million.
Drug Development Process and various approaches
The drug development process typically involves several stages: 1. Discovery and development, 2. preclinical research, 3. clinical trials, 4. phase-1, phase-2, phase-3, and regulatory review , 5. Approval and post marketing surveillance and Marketing and distribution
Natural products in drug discovery
Natural products are an important source for drug discovery. The drug discovery process involves several steps including target identification, validation, lead identification and optimization through screening compounds for activity against the target. Promising lead compounds then undergo preclinical testing in labs and animal models before progressing to human clinical trials. Computational tools also play an important role in drug design, such as identifying binding sites on target proteins and modeling molecular interactions to optimize lead compounds. Natural products, especially toxins from venom, continue to provide templates for rational drug design.
cadd_faizan.pptx
This document discusses various approaches to computer-aided drug design (CADD), including ligand-based, target-based, de novo, and structure-based approaches. It describes the key steps and goals of CADD, which aim to accelerate drug discovery through rational drug design and testing compared to random screening. Specific methods discussed include quantitative structure-activity relationship analysis, pharmacophore modeling, molecular docking, and analyzing protein structures to inform lead optimization. The overall goal of CADD is to speed drug discovery and remove unsuccessful drug candidates earlier.
DrugDesignandDiscover
The document summarizes a lecture on drug design and discovery. It discusses:
1) Drug sources can come from animals, plants, inorganic substances, or be synthesized. Membrane proteins, DNA, RNA, and enzymes are common drug targets.
2) Computer-aided drug design techniques like ligand-based drug design using QSARs and pharmacophores, and structure-based drug design using docking can aid in drug discovery.
3) The drug discovery process involves identifying a disease target, finding a lead compound, optimizing the lead through chemical modifications, and conducting preclinical and clinical trials which can take 10-12 years.
In silico lead discovery technique.pptx
This document discusses techniques for in silico lead discovery in drug development. It describes identifying a target and bioassay, finding a lead compound, isolating and purifying the compound, determining its structure, studying structure-activity relationships, and identifying the pharmacophore. Methods for identifying lead compounds include random screening, non-random screening, high-throughput screening, and structure-based drug design. After preclinical studies, compounds undergo clinical trials in four phases before potential release as an approved drug.
All you have to know about drugs
Drug discovery is a process that involves target identification, validation, lead identification and optimization, and preclinical and clinical testing in humans. The goal is to identify compounds that can safely and effectively treat diseases. Preclinical testing assesses safety and efficacy in cells and animals. If promising, an Investigational New Drug application is filed with the FDA and clinical trials in humans begin. Clinical trials have phases to test safety, efficacy, side effects, and optimal dosage in healthy volunteers and patients. If successful, a New Drug Application is filed with the FDA for review and potential approval.
Drug discovery infoghraph
Drug discovery is a process that involves target identification, validation, lead identification and optimization, and preclinical and clinical testing in humans. The goal is to identify compounds that can safely and effectively treat diseases. Preclinical testing assesses safety and efficacy in cells and animals. If promising, an Investigational New Drug application is filed with the FDA and clinical trials in humans begin. Clinical trials have phases to test safety, efficacy, side effects, and optimal dosage in healthy volunteers and patients. If successful, a New Drug Application is filed with the FDA to verify the drug is safe and effective for its proposed use.
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Lecture 8 drug targets and target identification
This document provides an overview of drug targets and target identification. It discusses the types of biological targets including proteins, nucleic acids, and small molecules. Common protein drug targets are G protein-coupled receptors, enzymes, ion channels, nuclear hormone receptors, structural proteins, and membrane transport proteins. The document describes characteristics of drug targets and examples of current drug targets such as receptors, enzymes, ion channels, nuclear hormone receptors, and membrane transport proteins. It also discusses the process of target identification including direct biochemical methods, computational inference methods, and genetic interaction methods as well as tools used for target identification like microarrays, antisense technologies, chemical genomics, and proteomics.
Lecture 13 – comparative modeling
Comparative modeling predicts the 3D structure of a target protein sequence based on its alignment to template protein structures of known structure. It consists of four main steps: fold assignment, alignment of the target and template sequences, building a model based on the alignment, and predicting errors in the model. Comparative modeling is often used to facilitate functional characterization of a protein when its experimental structure is unknown, as it can provide a useful 3D structural model for proteins related to templates.
Lecture 12 – chemoinformatic
This document provides an overview of chemoinformatics. It begins with an introduction to chemoinformatics, defining it as a field that uses computers to store and analyze large quantities of chemical data. It notes that central concepts in chemoinformatics like QSAR have been used for over 30 years, but the field is now playing a larger role in drug discovery. The document then discusses the differences between chemoinformatics, bioinformatics, and molecular modeling. It provides examples of important chemical databases and methods for searching databases and predicting compound properties in chemoinformatics.
Lecture 11 developing qsar, evaluation of qsar model and virtual screening
This document discusses the development and evaluation of quantitative structure-activity relationship (QSAR) models. It begins with a brief history of QSAR, including Hansch's early equation relating biological activity to hydrophobicity and electronic characteristics. Methods for calculating properties like log P partitioning are described. Validation of QSAR models involves statistical measures of goodness-of-fit and model stability as well as external validation. Applications of QSAR include drug design, toxicity prediction, and property prediction. Virtual screening uses computational methods to identify potential hit compounds from large databases.
Lecture 10 pharmacophore modeling and sar paradox
The document discusses pharmacophore modeling, which involves identifying the 3D arrangement of functional groups necessary for a molecule to bind to a target site and trigger a biological response. It notes that pharmacophore modeling is important for understanding receptor-ligand interactions and for drug design. Two types are described: ligand-based, which extracts common chemical features from known ligands in the absence of the target structure, and structure-based, which generates features from the target or target-ligand complex structure. Pharmacophore models can be used for virtual screening to identify molecules that encode the required interaction pattern. The document also discusses the structure-activity relationship and notes that similar molecules do not always have similar activities, known as the SAR paradox.
Lecture 9 molecular descriptors
This document discusses molecular descriptors, which are mathematical representations of molecular properties generated by algorithms. It covers the types of descriptors, including 1D, 2D and 3D descriptors. 1D descriptors represent information from molecular formulas, while 2D descriptors capture size, shape and electronic distribution. 3D descriptors describe properties related to 3D conformation. Tools discussed for calculating descriptors include commercial software as well as open-source options like CDK and PaDEL. The utility of descriptors in applications like QSAR modeling is also mentioned.
Lecture 7 computer aided drug design
This lecture discusses computer aided drug design (CADD). CADD uses computational methods to aid in drug discovery and design. It involves identifying drug targets, generating potential drug molecule "hits" through virtual screening, and optimizing "leads" through modeling protein-ligand interactions and simulating the effects of molecular modifications. The key steps are target identification, validation of targets, lead identification through screening libraries or de novo design, lead optimization through docking simulations, and preclinical/clinical trials. CADD approaches like ligand-based, target-based, and structure-based drug design can speed up the drug development process and reduce costs compared to traditional experimental methods alone.
Lecture 6 –active site identification
This document discusses identifying the active site of an enzyme. It begins by introducing the concepts of binding sites and how they relate to drug design. The active site is described as being a three-dimensional cleft formed by amino acid groups, taking up a small part of the enzyme's volume. Features of active sites include being clefts or crevices that bind substrates via weak attractions in a precisely defined arrangement. The document outlines steps for active site identification, including analyzing the protein structure to find the binding pocket and key interaction sites. It also discusses classifying ligand and protein atoms and characterizing the reconstructed binding pocket.
Lecture 5 pharmacophore and qsar
This document discusses pharmacophore identification and quantitative structure-activity relationships (QSAR). It defines a pharmacophore as specific arrangements of functional groups necessary for binding to macromolecules. Methods for pharmacophore identification include systematic search, distance geometry, and clique detection algorithms. QSAR employs statistics to investigate relationships between ligand structures and effects. 2D-QSAR links descriptors like hydrophobicity to activity, while 3D-QSAR better describes spatial arrangements using methods like comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA).
Lecture 4 ligand based drug design
This document discusses ligand-based drug design approaches. It defines ligand-based drug design as relying on knowledge of other molecules that bind to the biological target to derive a pharmacophore model or quantitative structure-activity relationship (QSAR) model. The most important method is pharmacophore modeling, which develops a model of interactions between ligands and the target receptor from the ligand perspective. Key ligand-based design approaches covered are pharmacophore modeling, QSAR, scaffold hoping, and pseudo receptors.
Lecture 3 rational drug design
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.
Lecture 2 history of drug designing and development
The document summarizes the history of drug design, beginning with drugs originating from natural sources like plants, animals, and microorganisms. It then discusses early milestones in drug development in the 19th century with the isolation of plant extracts and discovery of synthetic drug substances. Major developments are outlined for pain killers, cardiac drugs, antibiotics, anticancer agents, and endocrine therapies. Key events include the isolation of morphine, digitalis, penicillin, and insulin, as well as the discovery of major drug classes like aspirin, nitroglycerin, chloroquine, and statins. The history shows a progression from crude natural product drugs to purified and synthetic small molecule drugs targeted at specific biological pathways and receptors.
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Lecture 1 –Introduction to drug design and development
- 1. Lecture 1 –Introduction to drug design and development BTT- 516– Drug Designing and Development
- 2. Unit-A: Drug Design and Development •Introduction to drug design and development , History of drug designing, Rational drug design, Types of drug design- structure based and ligand based , Active site identification, Computer aided drug design. Unit-B: Drug target and identification •Drug Targets and Target identification , Drug target classification- DNA, RNA, Protein modifications/events, Post translational and processing enzymes, G protein coupled receptors (monomeric transmembraneproteins), Small molecule receptors and neuropeptide receptors, Ion channels,(monomeric multi transmembrane proteins), Proteins, transporters (multitransmembrane proteins). Unit-C: Pharmacophore and its modeling •Pharmacophore and its modeling, QSAR and its types, Developing QSAR, Molecular descriptors, SAR paradox and concept of force field, Lennard-Jones Potential , Evaluation of QSAR model and virtual screening , Applications of QSAR, basics of Chemoinformatics, Homology modeling. Unit-D: Computer aided drug designing •Computer aided drug design(CADD), Molecular docking, Different models for docking , Scoring function for docking, Comparative modeling Receptor fitting and receptor mapping. Syllabus
- 3. Topics to be covered today 1.Introduction to Drug designing and development –definition, history 2. Stages of New drug synthesis 3. Properties of an Ideal drug 4. Role of Bioinformatics in drug discovery 5. Homework
- 4. In the past most of drugs have been discovered by identifying the active ingredient from traditional remedies. Now we know that diseases are controlled at molecular and physiological levels. Also shape of molecules at atomic level are well known. Information of Human genome Introduction
- 6. Drug: Drug is any chemical substance that affects the functioning of living things and the organisms (such as bacteria, fungi, and viruses) that infect them. Pharmacology, the science of drugs, deals with all aspects of drugs in medicine, including their mechanism of action, physical and chemical properties, metabolism, therapeutics, and toxicity Discovery Phase: Identification of a new chemical/molecule as a potential therapeutic agent Development Phase: Compounds is tested for safety and for one or more clinical indication, and in suitable formulations and dosage form. Introduction to drug design and development
- 8. Stages of New drug synthesis Drug Discovery: Molecules are chosen on the basis of their pharmacological properties. Pre-clinical development: Non human studies, e.g. toxicity testing, pharmacokinetic analysis and formulation Clinical development: The selected compound is selected for efficacy and side effects
- 10. Properties of Ideal drug 1) It should not disturb the psychological processes 2) It should apply only at the place wanted. 3) It should be harmless to hosts but should destroy harmful Microbes 4) It should have minimum side effects Lipinski's rule states that, in general, an orally active drug has no more than one violation of the following criteria: •Molecular mass less than 500 Dalton •High lipophilicity (expressed as LogP less than 5) •Less than 5 hydrogen bond donors •Less than 10 hydrogen bond acceptors •Molar refractivity should be between 40-130 Lipinski's rule of five: it is given by Christopher A. Lipinski in 1997, It is also known as Pfizer's rule of five or simply the rule of five (RO5), is a rule of thumb to evaluate druglikeness, determine pharmacological or biological activity, physical properties of compound.
- 11. Role of Bioinformatics in drug discovery Bioinformatics play key role in various stages of drug discovery • It help in target identification • Computer bases screening of chemical compounds • Pharmacogenomics • Druglikeness • ADME/T
- 12. Thank you Er. Rajan Rolta Faculty of Applied Sciences and Biotechnology Shoolini University, Village Bhajol, Solan (H.P) +91-7018792621 (Mob No.) rajanrolta@shooliniuniversity.com