Chemoinformatics approaches to virtual screening / edited by Alexandre Varnek, Alex Tropsha.

Format:

Book

Contributor:

Varnek, Alexandre.

Tropsha, Alex.

Royal Society of Chemistry (Great Britain)

Language:

English

Subjects (All):

Cheminformatics.

Chemistry--Data processing.

Chemistry.

Physical Description:

1 online resource (xvi, 338 pages) : illustrations (some color).

Edition:

1st ed.

Place of Publication:

Cambridge : RSC Pub., 2008.

Language Note:

English

Summary:

Focuses on chemoinformatics approaches applicable to virtual screening of very large available collections of chemical compounds to identify novel biologically active molecules. Chemoinformatics is broadly a scientific discipline encompassing the design, creation, organization, management, retrieval, analysis, dissemination, visualization and use of chemical information. It is distinct from other computational molecular modeling approaches in that it uses unique representations of chemical structures in the form of multiple chemical descriptors; has its own metrics for defining similarity and diversity of chemical compound libraries; and applies a wide array of statistical, data mining and machine learning techniques to very large collections of chemical compounds in order to establish robust relationships between chemical structure and its physical or biological properties. Chemoinformatics addresses a broad range of problems in chemistry and biology; however, the most commonly known applications of chemoinformatics approaches have been arguably in the area of drug discovery where chemoinformatics tools have played a central role in the analysis and interpretation of structure-property data collected by the means of modern high throughput screening. Early stages in modern drug discovery often involved screening small molecules for their effects on a selected protein target or a model of a biological pathway. In the past fifteen years, innovative technologies that enable rapid synthesis and high throughput screening of large libraries of compounds have been adopted in almost all major pharmaceutical and biotech companies. As a result, there has been a huge increase in the number of compounds available on a routine basis to quickly screen for novel drug candidates against new targets/pathways. In contrast, such technologies have rarely become available to the academic research community, thus limiting its ability to conduct large scale chemical genetics or chemical genomics research. However, the landscape of publicly available experimental data collection methods for chemoinformatics has changed dramatically in very recent years. The term "virtual screening" is commonly associated with methodologies that rely on the explicit knowledge of three-dimensional structure of the target protein to identify potential bioactive compounds. Traditional docking protocols and scoring functions rely on explicitly defined three dimensional coordinates and standard definitions of atom types of both receptors and ligands. Albeit reasonably accurate in many cases, conventional structure based virtual screening approaches are relatively computationally inefficient, which has precluded them from screening really large compound collections. Significant progress has been achieved over many years of research in developing many structure based virtual screening approaches. This book is the first monograph that summarizes innovative applications of efficient chemoinformatics approaches towards the goal of screening large chemical libraries. The focus on virtual screening expands chemoinformatics beyond its traditional boundaries as a synthetic and data-analytical area of research towards its recognition as a predictive and decision support scientific discipline. The approaches discussed by the contributors to the monograph rely on chemoinformatics concepts such as: -representation of molecules using multiple descriptors of chemical structures -advanced chemical similarity calculations in multidimensional descriptor spaces -the use of advanced machine learning and data mining approaches for building quantitative and predictive structure activity models -the use of chemoinformatics methodologies for the analysis of drug-likeness and property prediction -the emerging trend on combining chemoinformatics and bioinformatics concepts in structure based drug discovery The chapters of the book are organized in a logical flow that a typical chemoinformatics project would follow - from structure representation and comparison to data analysis and model building to applications of structure-property relationship models for hit identification and chemical library design. It opens with the overview of modern methods of compounds library design, followed by a chapter devoted to molecular similarity analysis. Four sections describe virtual screening based on the using of molecular fragments, 2D pharmacophores and 3D pharmacophores. Application of fuzzy pharmacophores for libraries design is the subject of the next chapter followed by a chapter dealing with QSAR studies based on local molecular parameters. Probabilistic approaches based on 2D descriptors in assessment of biological activities are also described with an overview of the modern methods and software for ADME prediction. The book ends with a chapter describing the new approach of coding the receptor binding sites and their respective ligands in multidimensional chemical descriptor space that affords an interesting and efficient alternative to traditional docking and screening techniques. Ligand-based approaches, which are in the focus of this work, are more computationally efficient compared to structure-based virtual screening and there are very few books related to modern developments in this field. The focus on extending the experiences accumulated in traditional areas of chemoinformatics research such as Quantitative Structure Activity Relationships (QSAR) or chemical similarity searching towards virtual screening make the theme of this monograph essential reading for researchers in the area of computer-aided drug discovery. However, due to its generic data-analytical focus there will be a growing application of chemoinformatics approaches in multiple areas of chemical and biological research such as synthesis planning, nanotechnology, proteomics, physical and analytical chemistry and chemical genomics.

Contents:

Preface

1

Fragment Descriptors in SAR/QSAR/QSPR studies, molecular similarity analysis and in virtual screening

Introduction

Historical survey

Main characteristics of Fragment Descriptors

Types of Fragments

Simple Fixed Types

WLN and SMILES Fragments

Atom-Centered Fragments

Bond-Centered Fragments

Maximum Common Substructures

Atom Pairs and Topological Multiplets

Substituents and Molecular Frameworks

Basic Subgraphs

Mined Subgraphs

Random Subgraphs

Library Subgraphs

Fragments describing supramolecular systems and chemical reactions

Storage of fragments' information

Fragment's Connectivity

Generic Graphs

Labeling Atoms

Application in Virtual Screening and In Silico Design

Filtering

Similarity Search

SAR Classification (Probabilistic) Models

QSAR/QSPR Regression Models

In Silico Design

Limitations of Fragment Descriptors

Conclusion

2

Topological Pharmacophores

3D pharmacophore models and descriptors

Topological pharmacophores

Topological pharmacophores from 2D-aligments

Topological pharmacophores from 2D pharmacophore fingerprints

Topological index-based 'pharmacophores'?

Topological pharmacophores from pharmacophore fingerprints

Topological pharmacophore pair fingerprints

Topological pharmacophore triplets

Similarity searching with pharmacophore fingerprints

Technical Issues

Some Examples

Machine-learning of Topological Pharmacophores from Fingerprints

Conclusions

3

Pharmacophore-based Virtual Screening in Drug Discovery

Virtual Screening Methods

Chemical Feature-based Pharmacophores

The Term "3D Pharmacophore"

Feature Definitions and Pharmacophore Representation

Hydrogen bonding interactions

Lipophilic areas

Aromatic interactions

Charge-transfer interactions

Customization and definition of new features

Current super-positioning techniques for aligning 3D pharmacophores and molecules

Generation and Use of Pharmacophore Models

Ligand-based Pharmacophore Modeling

Structure-based Pharmacophore Modeling

Inclusion of Shape Information

Qualitative vs. Quantitative Pharmacophore Models

Validation of Models for Virtual Screening

Application of Pharmacophore Models in Virtual Screening

Pharmacophore Models as Part of a Multi-Step Screening Approach

Antitarget and ADME(T) Screening Using Pharmacophores

Pharmacophore Models for Activity Profiling and Parallel Virtual Screening

Pharmacophore Method Extensions and Comparisons to Other Virtual Screening Methods

Topological Fingerprints

Shape-based Virtual Screening

Docking Methods

Pharmacophore Constraints Used in Docking

Further Reading

Summary and Conclusion

4

Molecular Similarity Analysis in Virtual Screening

Ligand-Based Virtual Screening

Foundations of Molecular Similarity Analysis

Molecular Similarity and Chemical Spaces

Similarity Measures

Activity Landscapes

Analyzing the Nature of Structure-Activity Relationships

Relationships between different SARs

SARs and target-ligand interactions

Qualitative SAR characterization

Quantitative SAR characterization

Implications for molecular similarity analysis and virtual screening

Strengths and Limitations of Similarity Methods

Conclusion and Future Perspectives

5

Molecular Field Topology Analysis in drug design and virtual screening

Introduction: local molecular parameters in QSAR, drug design and virtual screening

Supergraph-based QSAR models

Rationale and history

Molecular Field Topology Analysis (MFTA)

General principles

Local molecular descriptors: facets of ligand-biotarget interaction

Construction of molecular supergraph

Formation of descriptor matrix

Statistical analysis

Applicability control

From MFTA model to drug design and virtual screening

MFTA models in biotarget and drug action analysis

MFTA models in virtual screening

MFTA-based virtual screening of compound databases

MFTA-based virtual screening of generated structure libraries

6

Probabilistic approaches in activity prediction

Biological Activity

Dose-Effect Relationships

Experimental Data

Probabilistic Ligand-Based Virtual Screening Methods

Preparation of Training Sets

Creation of Evaluation Sets

Mathematical Approaches

Evaluation of Prediction Accuracy

Single-Targeted vs. Multi-Targeted Virtual Screening

PASS Approach

Biological Activities Predicted by PASS

Chemical Structure Description in PASS

SAR Base

Algorithm of Activity Spectrum Estimation

Interpretation of Prediction Results

Selection of the Most Prospective Compounds

7

Fragment-based de novo design of druglike molecules

From Molecules to Fragments

From Fragments to Molecules

Scoring the Design

Conclusions and Outlook

8

Early ADME/T predictions: a toy or a tool?

Which properties are important for early drug discovery?

Physico-chemical profiling

Lipophilicity

Solubility

Data availability and accuracy

Models

Why models don't work: the challenge of the Applicability Domain

AD based on similarity in the descriptor space

AD based on similarity in the property-based space

How reliable are predictions of physico-chemical properties?

Available Data for ADME/T biological properties

Absorption

Data

Distribution

The usefulness of ADME/T models is limited by available data

9

Compound Library Design

Principles and Applications

Introduction to Compound Library Design

Methods for Compound Library Design

Design for Specific Biological Activities

Similarity Guided Design of Targeted Libraries

Diversity Based Design of General Screening Libraries

Pharmacophore Guided Design of Focused Compound Libraries

QSAR Based Targeted Library Design

Protein Structure Based Methods for Compound Library Design

Design for Developability or Drug-likeness

Rule & Alert Based Approaches

QSAR Based ADMET Models

Undesirable Functionality Filters

Design for Multiple Objectives and Targets Simultaneously

Concluding Remarks

10

Integrated Chemo- and Bioinformatics Approaches to Virtual Screening

Availability of large compound collections for virtual screening

NIH Molecular Libraries Roadmap Initiative and the PubChem database

Other chemical databases in public domain

Structure based virtual screening

Major methodologies

Challenges and limitations of current approaches

The implementation of cheminformatics concepts in structure based virtual screening

Predictive QSAR models as virtual screening tools

Critical Importance of model validation

Applicability domains and QSAR model acceptability criteria

Predictive QSAR modeling workflow

Examples of application

Structure based chemical descriptors of protein ligand interface: the EnTESS method

Derivation of the EnTESS descriptors

Validation of the EnTESS descriptors for binding affinity prediction

Structure based cheminformatics approach to virtual screening: the CoLiBRI method

The representation of three-dimensional active sites in multidimensional chemistry space

The mapping between chemistry spaces of active sites and ligands

Summary and Conclusions.

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

1-61583-352-8

1-84755-887-9

OCLC:

319517631