In Silico Medicinal Chemistry: Computational Methods to Support Drug Design
Covering computational tools in drug design using techniques from chemoinformatics, molecular modelling and computational chemistry, this book explores these methodologies and applications of in silico medicinal chemistry.
The first part of the book covers molecular representation methods in computing in terms of chemical structure, together with guides on common structure file formats. The second part examines commonly used classes of molecular descriptors. The third part provides a guide to statistical learning methods using chemical structure data. The fourth part of the book covers topics such as similarity searching, clustering and diversity selection, virtual library design, ligand docking and de novo design in the drug discovery setting. The final part of the book summarises the application of methods to the different stages of drug discovery, from target ID, through hit finding and hit-to-lead, to lead optimisation.
This book is a practical introduction to the subject for researchers new to the fields of chemoinformatics, molecular modelling and computational chemistry.
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2016 Published application approach appropriate atom binding bioisosteric bioisosteric replacement bromodomains calculated challenges chapter Chem ChEMBL chemical structures chemoinformatics coefficient compounds Computational Chemistry Computational Chemistry Series Computational Methods concept conformer considered database dataset defined Design By Nathan docking drug design drug discovery druggability encode exploration field Figure fingerprint given graph theory groups high-throughput screening hydrogen bond identify important InChI interactions isosterism ligand ligand docking ligand-based logP Methods to Support molecular descriptors molecular graph molecular similarity molecular structure molecules multiobjective Nathan Brown novo design objects optimisation Overview pharmacophore physicochemical potential predictions prioritise properties protein protein–ligand QSAR RDKit represented RSC Theoretical scaffold hopping scientists scoring functions screening libraries shape Silico Medicinal Chemistry similarity searching Society of Chemistry statistical learning methods struc subset substructure Support Drug Design synthesis Tanimoto target Theoretical and Computational three-dimensional tion topological descriptors typically unsupervised learning vector virtual screening Website www.rsc.org