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Computational Drug Discovery and Design

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Cover of 'Computational Drug Discovery and Design'

Table of Contents

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    Book Overview
  2. Altmetric Badge
    Chapter 1 Computer-Aided Drug Design: An Overview
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    Chapter 2 Prediction of Human Drug Targets and Their Interactions Using Machine Learning Methods: Current and Future Perspectives
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    Chapter 3 Practices in Molecular Docking and Structure-Based Virtual Screening
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    Chapter 4 Phylogenetic and Other Conservation-Based Approaches to Predict Protein Functional Sites
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    Chapter 5 De Novo Design of Ligands Using Computational Methods
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    Chapter 6 Molecular Dynamics Simulation and Prediction of Druggable Binding Sites
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    Chapter 7 Virtual Ligand Screening Using PL-PatchSurfer2, a Molecular Surface-Based Protein–Ligand Docking Method
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    Chapter 8 Fragment-Based Ligand Designing
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    Chapter 9 Molecular Dynamics as a Tool for Virtual Ligand Screening
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    Chapter 10 Building Molecular Interaction Networks from Microarray Data for Drug Target Screening
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    Chapter 11 Absolute Alchemical Free Energy Calculations for Ligand Binding: A Beginner’s Guide
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    Chapter 12 Evaluation of Protein–Ligand Docking by Cyscore
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    Chapter 13 Molecular Dynamics Simulations of Protein–Drug Complexes: A Computational Protocol for Investigating the Interactions of Small-Molecule Therapeutics with Biological Targets and Biosensors
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    Chapter 14 Prediction and Optimization of Pharmacokinetic and Toxicity Properties of the Ligand
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    Chapter 15 Protein–Protein Docking in Drug Design and Discovery
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    Chapter 16 Automated Inference of Chemical Discriminants of Biological Activity
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    Chapter 17 Computational Exploration of Conformational Transitions in Protein Drug Targets
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    Chapter 18 Applications of the NRGsuite and the Molecular Docking Software FlexAID in Computational Drug Discovery and Design
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    Chapter 19 Calculation of Thermodynamic Properties of Bound Water Molecules
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    Chapter 20 Enhanced Molecular Dynamics Methods Applied to Drug Design Projects
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    Chapter 21 AGGRESCAN3D: Toward the Prediction of the Aggregation Propensities of Protein Structures
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    Chapter 22 Computational Analysis of Solvent Inclusion in Docking Studies of Protein–Glycosaminoglycan Systems
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    Chapter 23 Understanding G Protein-Coupled Receptor Allostery via Molecular Dynamics Simulations: Implications for Drug Discovery
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    Chapter 24 Identification of Potential MicroRNA Biomarkers by Meta-analysis
Attention for Chapter 1: Computer-Aided Drug Design: An Overview
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Chapter title
Computer-Aided Drug Design: An Overview
Chapter number 1
Book title
Computational Drug Discovery and Design
Published in
Methods in molecular biology, January 2018
DOI 10.1007/978-1-4939-7756-7_1
Pubmed ID
Book ISBNs
978-1-4939-7755-0, 978-1-4939-7756-7
Authors

Alan Talevi

Abstract

The term drug design describes the search of novel compounds with biological activity, on a systematic basis. In its most common form, it involves modification of a known active scaffold or linking known active scaffolds, although de novo drug design (i.e., from scratch) is also possible. Though highly interrelated, identification of active scaffolds should be conceptually separated from drug design. Traditionally, the drug design process has focused on the molecular determinants of the interactions between the drug and its known or intended molecular target. Nevertheless, current drug design also takes into consideration other relevant processes than influence drug efficacy and safety (e.g., bioavailability, metabolic stability, interaction with antitargets).This chapter provides an overview on possible approaches to identify active scaffolds (including in silico approximations to approach that task) and computational methods to guide the subsequent optimization process. It also discusses in which situations each of the overviewed techniques is more appropriate.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 122 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 122 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 21 17%
Student > Master 17 14%
Researcher 12 10%
Student > Ph. D. Student 9 7%
Lecturer 6 5%
Other 15 12%
Unknown 42 34%
Readers by discipline Count As %
Chemistry 22 18%
Biochemistry, Genetics and Molecular Biology 17 14%
Pharmacology, Toxicology and Pharmaceutical Science 17 14%
Agricultural and Biological Sciences 5 4%
Chemical Engineering 4 3%
Other 13 11%
Unknown 44 36%