Computational methods in drug discovery.
TLDR
An overview of computational methods used in different facets of drug discovery and highlight some of the recent successes is presented, both structure-based and ligand-based drug discovery methods are discussed.Abstract:
The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.read more
Citations
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References
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Journal ArticleDOI
Flexible receptor docking for drug discovery.
TL;DR: This article focuses on reviewing ensemble docking as an approximate but inexpensive method to incorporate receptor flexibility in molecular docking, and outlines key features and recent advances of this method and points out problem areas that need to be addressed.
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Multi-Conformer Ensemble Docking to Difficult Protein Targets
Sally R. Ellingson,Yinglong Miao,Yinglong Miao,Jerome Baudry,Jerome Baudry,Jeremy C. Smith,Jeremy C. Smith +6 more
TL;DR: Docking results obtained from molecular dynamics simulation followed by selection of top-ranking compounds appears to be an effective strategy for increasing the number and diversity of hits when using docking to screen large libraries of chemicals against difficult protein targets.
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Modeling Protein-Ligand Binding by Mining Minima
TL;DR: The first application of the mining minima algorithm to protein-small molecule binding is presented, and the computed changes in configurational entropy upon binding fall roughly along the same entropy-energy correlation previously observed for smaller host-guest systems.
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From docking false-positive to active anti-HIV agent.
Gabriela Barreiro,Joseph T. Kim,Cristiano Ruch Werneck Guimarães,Christopher M. Bailey,Robert A. Domaoal,Ligong Wang,Karen S. Anderson,William L. Jorgensen +7 more
TL;DR: It is demonstrated that it is possible to learn from a formally unsuccessful virtual-screening exercise and, with the aid of computational analyses, to efficiently evolve a false positive into a true active.
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Support vector machines: development of QSAR models for predicting anti-HIV-1 activity of TIBO derivatives.
Rachid Darnag,E.L. Mostapha Mazouz,Andreea R. Schmitzer,Didier Villemin,Abdellah Jarid,Driss Cherqaoui +5 more
TL;DR: The proposed method can be successfully used to predict the anti-HIV of TIBO derivatives with only four molecular descriptors which can be calculated directly from molecular structure alone.
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