Cheminformatics combines chemistry, computer science, and information science to study large amounts of chemical information, mostly with computer assistance. It encompasses the design, creation, organization, storage, retrieval, analysis, and use of chemical data. Cheminformatics has various applications including drug discovery. It uses tools like databases, machine learning, molecular properties predictions, and information analysis to help identify new drug leads. Future trends include increased data integration, computer-assisted synthesis design, and expanded use of cheminformatics methods in theoretical chemistry and protein studies. Cheminformatics plays an important role in modern drug development.
Introduction
What is cheminformatics?
Why do we have to use informatics methods in chemistry?
Is it cheminformatics or chemoinformatics?
Emergence of cheminformations
Three major aspects of cheminformatics
Basics of cheminformatics
Topological representations
Tools used for cheminformatics
Application of cheminformatics
Role of cheminformatics in morden drug discovery
Conclusion
Bibliography
Cheminformatics, concept by kk sahu sirKAUSHAL SAHU
INTRODUCTION
THE NEED FOR CHEMOINFORMATICS
CHEMOINFORMATICS AND DRUG DISCOVERY
HISTORICAL EVOLUTION
BASIC CONCEPTS
Chemistry Space
Molecular Descriptors
High-Throughput Screening
The Similar-Structure, Similar-Property Principle
Graph theory and Chemoinformatics
CHEMOINFORMATICS TASKS
MOLECULAR REPRESENTATIONS
Topological Representations
Geometrical Representations
TYPES OF MOLECULAR DESCRIPTORS
IN SILICO DE NOVO MOLECULAR DESIGN
FREE CHEMISTRY DATABASE
FUTURE
CONCLUSION
REFERENCE
Cheminformatics is the application of computer science to solve chemical problems. It involves acquiring chemical data through experiments or simulations, managing the information in databases, and analyzing the data. Key aspects of cheminformatics include computer-assisted synthesis design, representing chemical structures digitally, and using mathematical models to analyze chemical data. Cheminformatics plays an important role in drug discovery by aiding processes like target identification, lead discovery, and molecular modeling.
An Introduction to Chemoinformatics for the postgraduate students of AgricultureDevakumar Jain
1. Chemoinformatics is the application of informatics methods to solve chemical problems and encompasses the design, creation, organization, management, retrieval, analysis, dissemination, visualization and use of chemical information.
2. It combines aspects of chemistry and computer science to address challenges such as representing and searching large chemical structure databases, predicting molecular properties, and aiding in drug discovery.
3. Chemoinformatics tools and methods have applications in diverse areas including organic synthesis, analytical chemistry, toxicology prediction, and agrochemical discovery.
Dr. Igor V. Tetko introduces chemoinformatics, which uses informatics methods to solve chemical problems. It involves organizing and analyzing large chemical datasets. Key applications include drug discovery, chemical safety assessments like REACH, and predictive toxicology. Chemoinformatics helps address issues like high drug development costs and testing requirements by predicting properties in silico. The course covers topics like molecular representations, modeling techniques, and the online OCHEM database and modeling platform. Chemoinformatics aims to transform chemical data into knowledge to make better informed decisions.
This document discusses the scope and applications of chemoinformatics. It outlines how chemoinformatics is used in drug design, clinical research, synthetic chemistry, pharmaceutical industries, pharmacogenomics, systems biology, and nanotechnology. Specifically, it describes how chemoinformatics provides virtual structure libraries, docking capabilities, and QSAR studies to aid in drug discovery and development. It also notes applications such as storage and retrieval of chemical data, common file formats, creation of virtual libraries, virtual screening, and using QSAR to predict compound activities from their structures.
Cheminformatics plays a key role in modern drug discovery by helping chemists organize and analyze the vast amounts of chemical data being produced. It combines fields like chemistry, biology, and informatics to transform data into knowledge. Specifically, cheminformatics aids in tasks like identifying drug targets, finding lead compounds, optimizing leads, and conducting pre-clinical trials through methods such as high-throughput screening, structure-activity modeling, and predictive toxicity analysis. It also provides tools for tasks like drawing and searching chemical structures in databases.
The document discusses the process of preparing a chemical database for virtual screening or compound acquisition. It begins with assembling collections from in-house and external databases. The collection is then cleaned by removing invalid structures and standardizing structure representations. Property filtering is used to focus on lead-like compounds. Known active molecules are searched for structural similarity. Alternative structures like stereoisomers are explored. Representatives are selected from clustered structures using descriptors and similarity metrics. 3D structures are generated and a final list of compounds is assembled for screening, with some random additions, completing the preparation.
Introduction
What is cheminformatics?
Why do we have to use informatics methods in chemistry?
Is it cheminformatics or chemoinformatics?
Emergence of cheminformations
Three major aspects of cheminformatics
Basics of cheminformatics
Topological representations
Tools used for cheminformatics
Application of cheminformatics
Role of cheminformatics in morden drug discovery
Conclusion
Bibliography
Cheminformatics, concept by kk sahu sirKAUSHAL SAHU
INTRODUCTION
THE NEED FOR CHEMOINFORMATICS
CHEMOINFORMATICS AND DRUG DISCOVERY
HISTORICAL EVOLUTION
BASIC CONCEPTS
Chemistry Space
Molecular Descriptors
High-Throughput Screening
The Similar-Structure, Similar-Property Principle
Graph theory and Chemoinformatics
CHEMOINFORMATICS TASKS
MOLECULAR REPRESENTATIONS
Topological Representations
Geometrical Representations
TYPES OF MOLECULAR DESCRIPTORS
IN SILICO DE NOVO MOLECULAR DESIGN
FREE CHEMISTRY DATABASE
FUTURE
CONCLUSION
REFERENCE
Cheminformatics is the application of computer science to solve chemical problems. It involves acquiring chemical data through experiments or simulations, managing the information in databases, and analyzing the data. Key aspects of cheminformatics include computer-assisted synthesis design, representing chemical structures digitally, and using mathematical models to analyze chemical data. Cheminformatics plays an important role in drug discovery by aiding processes like target identification, lead discovery, and molecular modeling.
An Introduction to Chemoinformatics for the postgraduate students of AgricultureDevakumar Jain
1. Chemoinformatics is the application of informatics methods to solve chemical problems and encompasses the design, creation, organization, management, retrieval, analysis, dissemination, visualization and use of chemical information.
2. It combines aspects of chemistry and computer science to address challenges such as representing and searching large chemical structure databases, predicting molecular properties, and aiding in drug discovery.
3. Chemoinformatics tools and methods have applications in diverse areas including organic synthesis, analytical chemistry, toxicology prediction, and agrochemical discovery.
Dr. Igor V. Tetko introduces chemoinformatics, which uses informatics methods to solve chemical problems. It involves organizing and analyzing large chemical datasets. Key applications include drug discovery, chemical safety assessments like REACH, and predictive toxicology. Chemoinformatics helps address issues like high drug development costs and testing requirements by predicting properties in silico. The course covers topics like molecular representations, modeling techniques, and the online OCHEM database and modeling platform. Chemoinformatics aims to transform chemical data into knowledge to make better informed decisions.
This document discusses the scope and applications of chemoinformatics. It outlines how chemoinformatics is used in drug design, clinical research, synthetic chemistry, pharmaceutical industries, pharmacogenomics, systems biology, and nanotechnology. Specifically, it describes how chemoinformatics provides virtual structure libraries, docking capabilities, and QSAR studies to aid in drug discovery and development. It also notes applications such as storage and retrieval of chemical data, common file formats, creation of virtual libraries, virtual screening, and using QSAR to predict compound activities from their structures.
Cheminformatics plays a key role in modern drug discovery by helping chemists organize and analyze the vast amounts of chemical data being produced. It combines fields like chemistry, biology, and informatics to transform data into knowledge. Specifically, cheminformatics aids in tasks like identifying drug targets, finding lead compounds, optimizing leads, and conducting pre-clinical trials through methods such as high-throughput screening, structure-activity modeling, and predictive toxicity analysis. It also provides tools for tasks like drawing and searching chemical structures in databases.
The document discusses the process of preparing a chemical database for virtual screening or compound acquisition. It begins with assembling collections from in-house and external databases. The collection is then cleaned by removing invalid structures and standardizing structure representations. Property filtering is used to focus on lead-like compounds. Known active molecules are searched for structural similarity. Alternative structures like stereoisomers are explored. Representatives are selected from clustered structures using descriptors and similarity metrics. 3D structures are generated and a final list of compounds is assembled for screening, with some random additions, completing the preparation.
This document discusses chemoinformatics and its role in distance education. It defines chemoinformatics as the application of informatics methods to solve chemical problems, involving the design, creation, organization, management, analysis and use of chemical information. The document then outlines proposed courses for a distance learning program in chemoinformatics, including introductory courses covering fundamental concepts as well as more advanced topics involving programming, databases, and molecular modeling. It concludes by discussing how chemoinformatics can help improve distance education programs in chemistry fields.
AACIMP 2010 Summer School lecture by Igor Tetko. "Physics, Сhemistry and Living Systems" stream. "Chemoinformatics" course.
More info at http://summerschool.ssa.org.ua
Applied Bioinformatics & Chemoinformatics: Techniques, Tools, and OpportunitiesHezekiah Fatoki
The computational methods for in silico drug discovery have been broadly categories into two fields bioinformatics and chemoinformatics. In case of bioinformatics, major emphasis is on identification and validation of drug targets, mainly based on functional/structural annotation of genomes. In case of chemoinformatics or pharmacoinformatics, major emphasis is on designing of drug molecules or ligands and their interaction with drug targets.
1. Bioinformatics uses computer science and information technology to analyze biological data and assist with drug discovery. It helps identify drug targets and design drug candidates.
2. The drug design process involves identifying a disease target, studying compounds of interest, detecting molecular disease bases, rational drug design, refinement, and testing. Bioinformatics tools assist with each step.
3. CADD uses computational methods to simulate drug-receptor interactions and is heavily dependent on bioinformatics tools and databases. It supports techniques like virtual screening, sequence analysis, homology modeling, and physicochemical modeling to aid drug development.
1. Pharmacophore mapping involves identifying common binding elements in active compounds, generating potential conformations, and determining the 3D spatial relationships between pharmacophoric elements.
2. Conformational searching is important for pharmacophore mapping to explore a molecule's energy surface and identify low-energy conformations. There are different approaches like systematic search, distance geometry, and molecular dynamics.
3. Systematic search deterministically varies torsion angles to generate conformations. Distance geometry randomly samples conformations and can consider flexibility across multiple molecules simultaneously. Clique detection searches for common inter-feature distance patterns within active molecules to identify pharmacophore combinations.
Mining frequent pattern is a NP-hard problem and has become a hot topic in recent researches. Moreover,
protein dataset contains distinct Pattern that can be used in many areas such as drug discovery, disease
prediction, etc. In early decades, pattern discovery and protein fold recognition was determined by
biophysics and biochemistry approach; and X-ray and NMR have been used for protein structure
prediction which are very expensive and time consuming while, a mathematical approach can reduce the
cost of such laboratory experiments. Many computer based tests have been applied for the protein fold
detection such as graph based algorithms and data mining viewpoints like classification or clustering, and
all have their advantages and drawbacks. Pattern matching in protein sequential dataset for fold
recognition plays a meaningful role in the field of bioinformatics since it evolved prediction of unknown
protein function. There are lots of pattern recognition algorithms but in this work we used PrefixSpan. The
reason of selecting this algorithm will be discussed below in section 2. For evaluating the result of
experiments we used SCOPE dataset which is a classified protein dataset and ASTRAL, a discriminative
sequential dataset of SCOPE.
STRUCTURE BASED DRUG DESIGN - MOLECULAR MODELLING AND DRUG DISCOVERYTHILAKAR MANI
This document discusses molecular modeling and structure-based drug design. It begins by outlining the general process of drug development from basic studies to clinical trials. It then discusses how structural bioinformatics can facilitate drug discovery through molecular design. Structure-based drug design starts with target identification and verification, then determining the 3D structure of the target protein. Key approaches discussed include structure-based ligand generation methods like docking and de novo design, as well as virtual library design and computer-aided drug design more broadly.
HMM’S INTERPOLATION OF PROTIENS FOR PROFILE ANALYSISijcseit
HMM has found its application in almost every field. Applying Hmm to biological sequences has its own
advantages. HMM’s being more systematic and specific, yield a result better than consensus techniques.
Profile HMMs use position specific scoring for the matching & substitution of a residue and for the
opening or extension of a gap. HMMs apply a statistical method to estimate the true frequency of a residue
at a given position in the alignment from its observed frequency while standard profiles use the observed
frequency itself to assign the score for that residue. This means that a profile HMM derived from only 10 to
20 aligned sequences can be of equivalent quality to a standard profile created from 40 to 50 aligned
sequences.
This document provides an overview of the field of chemoinformatics. It defines chemoinformatics as the combination of chemistry and information technology used to process and analyze chemical data. The document discusses common representations of molecules including 1D, 2D, and 3D formats. It also outlines several common file formats used in chemoinformatics like Mol, SDF, and SMILES. Finally, it describes several important chemical databases, including PubChem, ChemBank, ChEMBL, and DrugBank, and gives examples of chemoinformatics applications such as virtual screening and QSAR modeling.
Protein threading using context specific alignment potential ismb-2013Sheng Wang
This document summarizes work on protein structure prediction using threading and context-specific alignment potentials. It introduces the problem of predicting protein structure for distant homologs using threading approaches. The work presents a solution that models protein alignment as a conditional probability using a context-specific conditional neural field (CNF) model incorporating both local and global alignment information. Evaluation on 1000 test cases showed improved accuracy over HHpred, an established threading approach, demonstrating the effectiveness of the proposed context-specific alignment potential.
Bio inspiring computing and its application in cheminformaticsabdelazim Galal
This document discusses applying bio-inspired computing techniques to problems in cheminformatics. It begins with introductions to cheminformatics and bio-inspired computing. Popular bio-inspired algorithms like ant colony optimization are explained. The document outlines applications of bio-inspired approaches to tasks in cheminformatics like classification, clustering, and feature selection. It concludes by noting potential applications in drug discovery and design.
This document discusses homology modeling, which is a computational technique used to develop atomic-resolution models of proteins based on their amino acid sequences and known 3D structures of homologous proteins. It describes the key steps in homology modeling as template identification, target-template alignment, model building and refinement, and model validation. The advantages of homology modeling include that it is faster than experimental techniques. However, the accuracy depends on factors like the sequence identity between the target and template.
Canonicalized systematic nomenclature in cheminformaticsJeremy Yang
This document discusses canonicalization in chemoinformatics and new canonicalization tools from OpenEye. It reviews existing canonicalization methods like the Morgan algorithm and describes how OpenEye has implemented and expanded on these methods to canonicalize molecular structures, tautomers, and pKa states. OpenEye tools like OEChem and QuacPac can generate canonical SMILES, connection tables, and representations of different chemical forms and standard file formats.
Sample Work For Engineering Literature Review and Gap IdentificationPhD Assistance
Sample Work For Engineering Literature Review and Gap Identification - PhD Assistance - http://bit.ly/2E9fAVq
2.1 INTRODUCTION
2.2 RESEARCH GAPS IN EXISTING METHODS
2.3 OBJECTIVES OF THIS WORK
Read More : http://bit.ly/2Rl7XT5
#gapanalysis #strategicmanagement #datagapanalysis #gapanalysisppt #gapanalysishealthcare #gapanalysisfinance #gapanalysisEngineering
This document summarizes various virtual screening techniques used in drug discovery. It discusses ligand-based methods like similarity searching using 2D and 3D fingerprints, pharmacophore mapping. It also discusses structure-based methods like protein-ligand docking to predict binding poses and scores. Hybrid methods combining different techniques are also used. The document provides an overview of key virtual screening methods and their applications to enrich hit rates and select compounds for further testing from large libraries in an efficient manner during the drug discovery process.
Prediction of the three dimensional structure of a given protein sequence i.e. target protein from the amino acid sequence of a homologous (template) protein for which an X-ray or NMR structure is available based on an alignment to one or more known protein structures
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).
Chemoinformatics—an introduction for computer scientistsunyil96
Chemoinformatics is an interdisciplinary field that combines expertise from chemistry, biology, physics, and computer science. It aims to discover novel chemical entities that can be developed into new medical treatments. The field uses computational methods and tools to analyze large collections of molecules in order to facilitate drug discovery. This involves tasks like selecting compounds for screening libraries, analyzing results from high-throughput screening to identify hit compounds, and optimizing leads into drug candidates. While the field has existed for decades, it was only recently termed "chemoinformatics" and has grown significantly with the ability to now synthesize and test huge numbers of compounds computationally.
This document discusses cheminformatics and its applications. Cheminformatics combines chemistry and computer science to store and analyze chemical data for applications like drug discovery. It encompasses designing, organizing, analyzing and visualizing chemical information. Key topics covered include molecular representations, chemical databases, similarity searching, machine learning methods, and tools for molecular docking and drug discovery.
This document discusses chemoinformatics and its role in distance education. It defines chemoinformatics as the application of informatics methods to solve chemical problems, involving the design, creation, organization, management, analysis and use of chemical information. The document then outlines proposed courses for a distance learning program in chemoinformatics, including introductory courses covering fundamental concepts as well as more advanced topics involving programming, databases, and molecular modeling. It concludes by discussing how chemoinformatics can help improve distance education programs in chemistry fields.
AACIMP 2010 Summer School lecture by Igor Tetko. "Physics, Сhemistry and Living Systems" stream. "Chemoinformatics" course.
More info at http://summerschool.ssa.org.ua
Applied Bioinformatics & Chemoinformatics: Techniques, Tools, and OpportunitiesHezekiah Fatoki
The computational methods for in silico drug discovery have been broadly categories into two fields bioinformatics and chemoinformatics. In case of bioinformatics, major emphasis is on identification and validation of drug targets, mainly based on functional/structural annotation of genomes. In case of chemoinformatics or pharmacoinformatics, major emphasis is on designing of drug molecules or ligands and their interaction with drug targets.
1. Bioinformatics uses computer science and information technology to analyze biological data and assist with drug discovery. It helps identify drug targets and design drug candidates.
2. The drug design process involves identifying a disease target, studying compounds of interest, detecting molecular disease bases, rational drug design, refinement, and testing. Bioinformatics tools assist with each step.
3. CADD uses computational methods to simulate drug-receptor interactions and is heavily dependent on bioinformatics tools and databases. It supports techniques like virtual screening, sequence analysis, homology modeling, and physicochemical modeling to aid drug development.
1. Pharmacophore mapping involves identifying common binding elements in active compounds, generating potential conformations, and determining the 3D spatial relationships between pharmacophoric elements.
2. Conformational searching is important for pharmacophore mapping to explore a molecule's energy surface and identify low-energy conformations. There are different approaches like systematic search, distance geometry, and molecular dynamics.
3. Systematic search deterministically varies torsion angles to generate conformations. Distance geometry randomly samples conformations and can consider flexibility across multiple molecules simultaneously. Clique detection searches for common inter-feature distance patterns within active molecules to identify pharmacophore combinations.
Mining frequent pattern is a NP-hard problem and has become a hot topic in recent researches. Moreover,
protein dataset contains distinct Pattern that can be used in many areas such as drug discovery, disease
prediction, etc. In early decades, pattern discovery and protein fold recognition was determined by
biophysics and biochemistry approach; and X-ray and NMR have been used for protein structure
prediction which are very expensive and time consuming while, a mathematical approach can reduce the
cost of such laboratory experiments. Many computer based tests have been applied for the protein fold
detection such as graph based algorithms and data mining viewpoints like classification or clustering, and
all have their advantages and drawbacks. Pattern matching in protein sequential dataset for fold
recognition plays a meaningful role in the field of bioinformatics since it evolved prediction of unknown
protein function. There are lots of pattern recognition algorithms but in this work we used PrefixSpan. The
reason of selecting this algorithm will be discussed below in section 2. For evaluating the result of
experiments we used SCOPE dataset which is a classified protein dataset and ASTRAL, a discriminative
sequential dataset of SCOPE.
STRUCTURE BASED DRUG DESIGN - MOLECULAR MODELLING AND DRUG DISCOVERYTHILAKAR MANI
This document discusses molecular modeling and structure-based drug design. It begins by outlining the general process of drug development from basic studies to clinical trials. It then discusses how structural bioinformatics can facilitate drug discovery through molecular design. Structure-based drug design starts with target identification and verification, then determining the 3D structure of the target protein. Key approaches discussed include structure-based ligand generation methods like docking and de novo design, as well as virtual library design and computer-aided drug design more broadly.
HMM’S INTERPOLATION OF PROTIENS FOR PROFILE ANALYSISijcseit
HMM has found its application in almost every field. Applying Hmm to biological sequences has its own
advantages. HMM’s being more systematic and specific, yield a result better than consensus techniques.
Profile HMMs use position specific scoring for the matching & substitution of a residue and for the
opening or extension of a gap. HMMs apply a statistical method to estimate the true frequency of a residue
at a given position in the alignment from its observed frequency while standard profiles use the observed
frequency itself to assign the score for that residue. This means that a profile HMM derived from only 10 to
20 aligned sequences can be of equivalent quality to a standard profile created from 40 to 50 aligned
sequences.
This document provides an overview of the field of chemoinformatics. It defines chemoinformatics as the combination of chemistry and information technology used to process and analyze chemical data. The document discusses common representations of molecules including 1D, 2D, and 3D formats. It also outlines several common file formats used in chemoinformatics like Mol, SDF, and SMILES. Finally, it describes several important chemical databases, including PubChem, ChemBank, ChEMBL, and DrugBank, and gives examples of chemoinformatics applications such as virtual screening and QSAR modeling.
Protein threading using context specific alignment potential ismb-2013Sheng Wang
This document summarizes work on protein structure prediction using threading and context-specific alignment potentials. It introduces the problem of predicting protein structure for distant homologs using threading approaches. The work presents a solution that models protein alignment as a conditional probability using a context-specific conditional neural field (CNF) model incorporating both local and global alignment information. Evaluation on 1000 test cases showed improved accuracy over HHpred, an established threading approach, demonstrating the effectiveness of the proposed context-specific alignment potential.
Bio inspiring computing and its application in cheminformaticsabdelazim Galal
This document discusses applying bio-inspired computing techniques to problems in cheminformatics. It begins with introductions to cheminformatics and bio-inspired computing. Popular bio-inspired algorithms like ant colony optimization are explained. The document outlines applications of bio-inspired approaches to tasks in cheminformatics like classification, clustering, and feature selection. It concludes by noting potential applications in drug discovery and design.
This document discusses homology modeling, which is a computational technique used to develop atomic-resolution models of proteins based on their amino acid sequences and known 3D structures of homologous proteins. It describes the key steps in homology modeling as template identification, target-template alignment, model building and refinement, and model validation. The advantages of homology modeling include that it is faster than experimental techniques. However, the accuracy depends on factors like the sequence identity between the target and template.
Canonicalized systematic nomenclature in cheminformaticsJeremy Yang
This document discusses canonicalization in chemoinformatics and new canonicalization tools from OpenEye. It reviews existing canonicalization methods like the Morgan algorithm and describes how OpenEye has implemented and expanded on these methods to canonicalize molecular structures, tautomers, and pKa states. OpenEye tools like OEChem and QuacPac can generate canonical SMILES, connection tables, and representations of different chemical forms and standard file formats.
Sample Work For Engineering Literature Review and Gap IdentificationPhD Assistance
Sample Work For Engineering Literature Review and Gap Identification - PhD Assistance - http://bit.ly/2E9fAVq
2.1 INTRODUCTION
2.2 RESEARCH GAPS IN EXISTING METHODS
2.3 OBJECTIVES OF THIS WORK
Read More : http://bit.ly/2Rl7XT5
#gapanalysis #strategicmanagement #datagapanalysis #gapanalysisppt #gapanalysishealthcare #gapanalysisfinance #gapanalysisEngineering
This document summarizes various virtual screening techniques used in drug discovery. It discusses ligand-based methods like similarity searching using 2D and 3D fingerprints, pharmacophore mapping. It also discusses structure-based methods like protein-ligand docking to predict binding poses and scores. Hybrid methods combining different techniques are also used. The document provides an overview of key virtual screening methods and their applications to enrich hit rates and select compounds for further testing from large libraries in an efficient manner during the drug discovery process.
Prediction of the three dimensional structure of a given protein sequence i.e. target protein from the amino acid sequence of a homologous (template) protein for which an X-ray or NMR structure is available based on an alignment to one or more known protein structures
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).
Chemoinformatics—an introduction for computer scientistsunyil96
Chemoinformatics is an interdisciplinary field that combines expertise from chemistry, biology, physics, and computer science. It aims to discover novel chemical entities that can be developed into new medical treatments. The field uses computational methods and tools to analyze large collections of molecules in order to facilitate drug discovery. This involves tasks like selecting compounds for screening libraries, analyzing results from high-throughput screening to identify hit compounds, and optimizing leads into drug candidates. While the field has existed for decades, it was only recently termed "chemoinformatics" and has grown significantly with the ability to now synthesize and test huge numbers of compounds computationally.
This document discusses cheminformatics and its applications. Cheminformatics combines chemistry and computer science to store and analyze chemical data for applications like drug discovery. It encompasses designing, organizing, analyzing and visualizing chemical information. Key topics covered include molecular representations, chemical databases, similarity searching, machine learning methods, and tools for molecular docking and drug discovery.
ANALYSIS OF PROTEIN MICROARRAY DATA USING DATA MININGijbbjournal
Latest progress in biology, medical science, bioinformatics, and biotechnology has become important and
tremendous amounts of biodata that demands in-depth analysis. On the other hand, recent progress in data
mining research has led to the development of numerous efficient and scalable methods for mining
interesting patterns in large databases. This paper bridge the two fields, data mining and bioinformatics
for successful mining of biological data. Microarrays constitute a new platform which allows the discovery
and characterization of proteins.
This document discusses the role and methods of systems biology in drug discovery and development. It covers key topics such as:
- The challenges of interpreting large omics data sets and how systems biology aims to integrate multi-omics data.
- Examples of how systems biology approaches like computational modeling can be used in target discovery, understanding drug mechanisms of action, predicting drug combinations, and more.
- How systems biology methods that combine experimental data with modeling are being applied across various stages of the drug development process from preclinical research to determining side effects.
Statistical modeling in pharmaceutical research and developmentPV. Viji
Statistical modeling in pharmaceutical research and development , Statistical Modeling , Descriptive Versus Mechanistic Modeling , Statistical Parameters Estimation , Confidence Regions , Non Linearity at the Optimum , Sensitivity Analysis , Optimal Design , Population Modeling
This document discusses the use of computers in pharmaceutical research and development. It begins by explaining how computers have transformed drug development processes by facilitating data storage, online literature searches, and computational modeling approaches. The document then provides a history of computers in pharmaceutical R&D from the 19th century to present day. It describes how early quantitative structure-activity relationship methods laid the groundwork for computer-aided drug design. The document outlines key developments from the 1960s to the 1990s that established computational techniques in major pharmaceutical companies. It distinguishes between descriptive and mechanistic modeling approaches. Finally, it discusses statistical modeling and parameters estimation techniques used in pharmaceutical research.
In this presentation, Shanthi describes what is called the rosetta model that uses cloud computing to show 3d models of chemical compounds. Shanthi says that such visualizations help in discovery of new proteins and drugs. Her interest area lies in the same domain.
Statistical modeling in pharmaceutical research and development.ANJALI
Statistical modeling in pharmaceutical research and development. This modelling is used in pharmaceutical industries to overcome the challenges related to pharmaceutical formulation, to reduce cost and increase quality and speed of pharmaceutical products.
This document describes two challenges presented as part of the DREAM initiative to evaluate methods for parameter estimation and network topology inference from experimental data. In the first challenge, participants were given the topology of a 9-gene network and asked to estimate 45 kinetic parameters. In the second challenge, participants were given an incomplete 11-gene network and asked to identify 3 missing links and associated parameters. Participants could purchase simulated experimental data using a credit system, allowing iterative experimental design. While parameter estimation was accomplished well using fluorescence data, topology inference was more difficult. Aggregating submissions produced better solutions than individual methods.
This document discusses cheminformatics, which involves the use of computer software and data analysis to study chemical compounds and their properties. It defines cheminformatics as combining chemical synthesis, biological screening, and data mining for drug discovery. The document outlines the history and evolution of the field from chemical information to cheminformatics. It also discusses various companies involved in cheminformatics and how it applies quantitative structure-activity relationships and other methods to guide drug development.
Bioinformatics plays an important role in drug discovery and development by enabling target identification, rational drug design, compound refinement, and other processes. Key applications of bioinformatics include virtual screening of large compound libraries to identify potential drug leads, homology modeling of protein structures to inform drug design, and similarity searches to find analogs of existing drug molecules. The overall drug development process involves studying the disease, identifying drug targets, designing compounds, testing and refining candidates, and conducting clinical trials. Computational techniques expedite many steps but experimental validation is still needed.
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.
Predicting active compounds for lung cancer based on quantitative structure-a...IJECEIAES
This document describes a study that uses machine learning models to predict active compounds for lung cancer. Specifically:
1) A dataset of molecules was collected from the ChEMBL database and divided into active and non-active groups based on inhibition concentration values. Molecular descriptors were then calculated to encode the chemical structures.
2) Two machine learning models - a neural network and gradient boosting tree classifier - were trained on the molecular descriptors to predict compound activity. Feature selection was also performed to analyze important structural features.
3) The models accurately predicted active compounds for lung cancer based on quantitative structure-activity relationships. Comparative analysis identified important chemical structures contributing to compound effectiveness.
Computational biology involves using computational techniques like data analysis, modeling and simulation to study biological systems. Bioinformatics specifically develops tools to analyze biological data. Other computational biology fields include computational anatomy, genomics, neuroscience, pharmacology, and evolutionary biology which all apply computational methods to study anatomical structures, genomes, the brain, drug effects, and evolution respectively. Cancer computational biology aims to predict cancer mutations by analyzing large biological datasets.
This document discusses how bioinformatics tools can be used in drug design. It describes several approaches: chemical modification of existing drugs, receptor-based design by determining receptor structures, and ligand-based design using known active ligands. It also discusses identifying disease targets, refining drug structures, detecting drug binding sites using protein modeling, and rational drug design techniques like virtual screening. QSAR methods relate compound structures to activities, while molecular modeling and docking simulate drug-receptor interactions to aid design. Informatics plays a key role in storing and analyzing the large amounts of data generated.
computer simulation in pharmacokinetics and pharmacodynamicsSUJITHA MARY
This document discusses the use of computer simulation in pharmacokinetics and pharmacodynamics at four different levels: whole organism, isolated tissues/organs, cellular, and protein/gene levels. At each level, mathematical models are used to represent biological processes and predict behavior over time. The goal is to better understand drug behavior and improve drug development by replacing animal and human trials with computer simulations. Challenges include integrating data from different structural levels and ensuring high quality input data.
This document summarizes different levels of computer simulations used in pharmacokinetics and pharmacodynamics:
1. Level 1 involves simulating the whole organism using systems of differential equations to model pharmacokinetic-pharmacodynamic relationships. These models can generate synthetic clinical trial data.
2. Level 2 simulates isolated tissues and organs using more detailed distributed parameter models to better represent physiological processes than lumped parameter whole-body models.
3. Level 3 simulates cells using complex models of intracellular processes, signaling networks, and membrane transport, though cellular mechanisms are still not fully known.
4. Level 4 involves computational design of proteins and genes, with the challenge of integrating information across multiple structural levels
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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3. DRUG DISCOVERY AND DEVELOPMENT
PROCESS OF DRUG DEVELOPMENT
TOOLS USED FOR DRUG DEVELOPMENT
APPLICATIONS OF DRUG DISCOVERY IN
MODERN ERA
CONCLUSION
HISTORY OF CHEMOINFORMATICS
WHAT IS CHEMOINFORMATICS?
MAJOR ASPECTS OF CHEMOINFORMATICS
COMMON CHEMINFORMATICS PATTERN
APPLICATIONS OF CHEMOINFORMATICS
CHALLENGES TO CHEMOINFORMATICS
CHEMINFORMATICS AND OTHER DISCIPLINES
FUTURE TRENDS OF CHEMINFOMATICS
Contents
4. HISTORY OF
CHEMOINFORMATICS
Cheminformatics is the mixing of those
information resources to transform data into
information and information into knowledge for
the intended purpose of making better
decisions faster in the area of drug lead
identification and optimization.
The term cheminformatics was defined in its
application to drug discover, for instance, by
F.K. Brown in 1998
Cheminformatics combines the scientific
working fields of chemistry, computer science,
and information science—for example in the
areas of topology, chemical graph
theory, information retrieval and data mining in
the chemical space. Cheminformatics can also
be applied to data analysis for various
industries like paper and pulp, dyes and such
allied industries.
5. WHAT IS CHEMOINFORMATICS ?
.
WHY DO WE NEED
CHEMOINFORMATICS
? To get funding
To handle large amount of information
Data information knowledge
To move chemistry into the computer age
Cheminformatics (also known
as cheminformatics and chemic
al informatics) is the study
of large amounts of chemical
information. It is done mostly
with the help of computers.
These tools are used
by pharmaceutical companies
to discovery new drugs.
Cheminformatics
encompasses the design ,
creation , organization
,management , retrieval ,
analysis , dissemination ,
visualization , and use of
chemical information.
To move from data to knowledge
Measurements/Calculations
6. MAJOR ASPECTS OF CHEMOINFORMATICS
Databases: Development of databases
for storage and retrieval of small
molecule structures and their properties.
04
Machine learning: Training of decision trees,
neural networks, self organizing maps, etc.
on molecular data.
05
Predictions: Molecular properties relevant to drugs,
virtual screening of chemical libraries, system
chemical biology networks.
06
Information Acquisition is a process of generating
and collecting data empirically or from theory
(molecular simulation)
01
Information management deals with
storage and retrieval of information.
02
Information use which includes Data Analysis,
correlation, and application to problems in the
chemical and biochemical sciences.
03
7. Markush
structure or
generic
structure
This is a topological pattern
used by chemists for many
years. It is determined by
experience. It is an efficient
way to represent an
unlimited number of
compounds with the same
scaffold. Additional
restrictions can be applied to
make the pattern more
specific. It is suitable for lead
optimization and hit-to-lead
efforts.
Fingerprint This is the topological pattern
systematically generated
from an algorithm. This
pattern has no human bias,
but can be meaningless to
chemistry. It is used in HTS
data mining.
Three-
dimensional
pharmacophore
This pattern is derived,
manually or computationally,
from a three-dimensional
molecular model. The
pattern is based upon a
physical model and binding
mechanism. It is sensitive to
conformation changes.
Better results are obtained
when supported by crystal or
NMR structural data. It is
suitable for lead
optimization.
COMMON CHEMINFORMATICS PATTERNS
8. Regression Regression methods are the most
traditional approaches for pattern
recognition. These methods assume
the variables are continuous and
the curve shapes are pre-defined.
For multidimensional data, curve
patterns are not known and trying
all possible curves is very time
consuming. In these cases, genetic
algorithms may be applied to
partially solve the problem of
identifying curve patterns.
Decision tree
classification
This approach is applied when there
are a great number of descriptors
and, the descriptors have various
value types and ranges.
Hierarchical
clustering
This approach assumes the objects
have hierarchical characters. The
methods require similarity or
distance matrices. The approach
may produce multiple answers for
users to explain or with which to
experiment.
Non-
hierarchical
clustering
The approach assumes the objects
have non-hierarchical characters,
and the number of clusters is
known prior the computation. The
method requires similarity or
distance matrices. The approach
may produce multiple answers for
users to explain or with which to
experiment.
9. APPLICATIONS OF CHEMOINFORMATICS
QUANTITATIVE STRUCTURE
ACTIVITY RELATIONSHIP :
predict activities of compounds
from their structure.
VIRTUAL LIBRARIES :
chemical data can pertain to
real or virtual molecules.
VIRTUAL SCREENING :
identification of novel active
molecules in large compound
databases.
STORAGE AND RETRIEVAL :
helps in storage , indexing &
search of information.
FILE FORMATS : uses
formats like SDF:2D &
3D, Mol:2D,
Mol2:3D,Xml
10. C H A L L E N G E S
TO
CHEMOINFORMATICS
Three Fundamental Questions
of A Chemist:
The fundamental and
lasting objective of synthesis is
not production of new
compounds but production of
properties.
If this is accepted, chemists
have to face three fundamental
questions.
a. Which compound will
have the desired property?
b. How can I make this
compound?
c. Did I make this
compound (what is the product
of my reaction)?
Toxicity Prediction and Risk
Assessment:
Society has become increasingly
interested and concerned about the
impact of chemicals on the
environment and on human health.
Therefore, chemicals should be
introduced into the market or used
only if they have been proven to be
safe.
Furthermore, the impact of
chemicals on the environment is of
much concern in society. Models for
persistence, bioaccumulation and
toxicity of chemicals in all kinds of
organisms are asked for.
Modeling Biological Systems:
The next step is then a focus on unraveling the events
in living organisms. It should be realized that life is
maintained by (bio)chemical reactions; modeling and
understanding them is essential for getting deeper
insights into the events that keep living species alive.
This could provide a basis for curing diseases. New
research fields have been conceived of, with names
such as systems chemistry, systems biology and
systems chemical biology. Whatever the names, the
aim is to further an understanding of biological
systems even to a point that they can be altered. A
collaboration between cheminformatics and
bioinformatics is essential for this purpose.
The newest developments aim at modeling entire
human organs. Large-scale government-supported
projects in the U.S. and in Germany have been
initiated to develop models for the human liver, the
virtual liver v-Liver at EPA [ and the German virtual
liver project networks
11. CHEMOINFORMATICS AND OTHER DISCIPLINES
Finally, one can use the relationships
between different properties issued
from physicochemical theory. (For
example, the Arrhenius law could be
particularly useful upon the modelling the
rate constants). These relationships
could be integrated into
cheminformatics workflow as an external
knowledge.
The second important distinction comes from the
fact that the chemical data result from an explorative
process in a huge chemical space rather than from
specially organized sampling. Hence, they cannot
be considered as representative, independent
and identically distributed sampling from a well
defined distribution. Thus, special approaches are
Cheminformatics as a Theoretical Chemistry
Discipline needed to treat this problem: various
strategies to explore chemical space, the
“applicability domain” concept, the active learning
approach, etc.
Cheminformatics and Bioinformatics
Unlike cheminformatics dealing with “chemical size”
molecules, bioinformatics uses computational tools to study
the structure and function of biomolecules (proteins, nucleic
acids). This is a broad field mostly involving 3D (force field
and quantum mechanics calculations) and 1D (sequence
alignment) modeling. In the latter, a biomolecule is
represented as a string of characters (building blocks).
Graph and fixed size vector models used in cheminformatics
are very rarely used in bioinformatics. In this sense, chemo
and bioinformatics are “complementary”.
Cheminformatics and Machine learning although
machine learning is widely used for structure property
modelling, cheminformatics can be considered as a very
specific area of its application. The specificity of
cheminformatics results from (i) the nature of chemical
objects, (ii) the complexity of the chemical universe and
(iii) a possibility to take into account an extra-knowledge.
The basic chemical object is a graph (or hyper graph),
rather than simple fixed-sized vector of numbers as in
the typical applications in mathematical statistics and
machine learning. This dictates the need to apply graph
theory, to develop novel descriptors and structured
graph kernels, and to apply machine learning methods
capable of dealing with structured discrete data.
12. FUTURE TRENDS OF CHEMINFORMATICS
5. Text and image mining, automatic extraction of
useful information from publications and patents.
6. Integration with bioinformatics, with focus on
ligand protein interactions and pharmacophores
7. Disappearing border between cheminformatics
and computational chemistry
8. In technology area –modularization, web services
9.Open source collaborative software development
10. Using all the advanced cheminformatics system,
it enhances the drug discovery rapidly and with low
cost and helps to eminent scientists to synthesize
the chemical molecules which lead to helps the
society.
1. Global databases, integration of multiple data
sources, public (Wikipedia-like) curation.
2. Use of Computer Assisted Structure
Elucidation (CASE) process and Computer
Assisted Synthesis Design (CASD) would be
integrated into the daily work process of bench
chemists.
3. Cheminformatics methods will be extended to
theoretical chemistry, stimulation of reaction;
study of proteins will be the future areas of thrust
for cheminformatics.
4. Use of large chemo genomics databases
(WOMBAT, GVK …)
14. DRUG DISCOVERY AND DEVELOPMENT
Drug is an active chemical substance used for diagnosis, mitigation,
treatment and prevention(DMTP) of diseases of humans and animals.
Also includes chemical substances, diagnostic, abortive and
contraceptive substances.
Drug discovery is the process of identifying new medicines.
Drug discovery take years to decade for discovering a new drug
and very costly.
It involves a wide range of biological, chemical and
pharmacological disciplines.
16. TOOLS USED FOR
DRUG DEVELOPMENT
The development of software and tools for computer assisted
organic synthesis are under vast development. This has
resulted in many tools and representations for chemical
structures. Some of the tools are listed below :
Chemdraw
Chemwindow
Chemreader
Chemsketch
logchem
wendi
chemmine
pubchem
open babel
Some other tools such as, CAS Draw, DIVA (Diverse
Information, Visualization and Analysis), Structure
Checker Accord, DS Accord Chemistry Cartridge,
MarvinSketch PowerMV, TINKER, APBS, ArgusLab,
Babel, ioSolveIT, ChemTK, Chimera, CLIFF, Dragon,
gOpenMol, Grace, JOELib, Jmol, IA_LOGP, Lammps,
MIPSIM, Mol2Mol, AMSOL, MOLCAS, Molexel, ICM-
Pro, ORTEP, Packmol, Polar, XLOGP,PREMIER
Biosoft, Q-chem, ALOGPS, Qmol, SageMD, ChemTK
Lite, Transient, CLOGP,TURBOMOLE, UNIVIS, VMD,
WHATIF, GCluto, COSMOlogic, KOWWIN are also
used.
17. APPLICATIONS OF DRUG DISCOVERY IN MODERN ERA
1 2 3 4
Drugs function by
interacting with
multiple protein
targets to create a
molecular interaction
signature that can be
exploited for rapid
therapeutic
repurposing and
discovery.
Developers designed
and synthesized a
series of nine
enmein-type ent-
kaurane diterpenoid
and furoxan-based
nitric oxide (NO)
donor hybrids from
commercially
available oridonin.
Feng Xu and coauthors
employed the 3D culture of
MCF-7 and SMMC-7721
cells based on the hanging
drop method and evaluated
the anti-proliferative activity
and cellular uptake of two
promising anti-tumor drug
candidates, evodiamine
(EVO) and rutaecarpine
(RUT), in 3D multicellular
spheroids and compared the
results with those obtained
from 2D monolayers
Rizk E. Khidre and
colleagues designed
and synthesized a
novel series of
quinoline compounds
and screened for
their antimalarial
activities, with the
hope that these
compounds could
lead to the
availability of better
drugs to treat
malaria.
5
Jun-Ru Wang and coauthors studied
a new series of ester derivatives of
10-hydroxycanthin-6-one using a
simple and effective synthetic
route as part of their continuing
research on canthin-6-one
antimicrobial agents. They
characterized the structure and
antimicrobial activity of each
compound, investigated the
structure-activity relationship, and
identified the promising lead
compound that had significant
antimicrobial activity against all the
fungi and bacterial strains tested for
the development of novel canthine-
6-one antimicrobial agents.
18. Cheminformatics can hence be described as the application of
informatics methods to solve chemical problems. It has developed
over the last 40 years to a mature discipline that has applications in
many areas of chemistry. It is an important scientific discipline that
stands on the interface between chemistry, biology and Information
Technology. Cheminformatics spans a very broad range of
problems and approaches which are often inter-related and
sometimes difficult to categorize. As high throughput technologies
and combinatorial chemistry continue to advance, informatics
techniques will become indispensable in managing and analyzing
the exploding volumes of data. By organizing, the data,
Cheminformatics will further introduce advancements in chemistry
and open new possibilities in the field of drug discovery. There are
still many problems that await a solution and therefore many new
developments in cheminformatics are foreseen. We believe that
this review will be the defining theme and might help to provide
much new advancement in the field of cheminformatics in coming
years. Hopefully, the availability of information related to
cheminformatics will catalyze further advancements and would
open new advancements in this field.
CONCLUSION