Journal Article10.1038/NRD1549
Docking and scoring in virtual screening for drug discovery: methods and applications.
TL;DR: Key concepts and specific features of small-molecule–protein docking methods are reviewed, selected applications are highlighted and recent advances that aim to address the acknowledged limitations of established approaches are discussed.
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Abstract: Computational approaches that 'dock' small molecules into the structures of macromolecular targets and 'score' their potential complementarity to binding sites are widely used in hit identification and lead optimization Indeed, there are now a number of drugs whose development was heavily influenced by or based on structure-based design and screening strategies, such as HIV protease inhibitors Nevertheless, there remain significant challenges in the application of these approaches, in particular in relation to current scoring schemes Here, we review key concepts and specific features of small-molecule-protein docking methods, highlight selected applications and discuss recent advances that aim to address the acknowledged limitations of established approaches
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Universal fragment descriptors for predicting properties of inorganic crystals
TL;DR: Data from the AFLOW repository for ab initio calculations is combined with Quantitative Materials Structure-Property Relationship models to predict important properties: metal/insulator classification, band gap energy, bulk/shear moduli, Debye temperature and heat capacities.
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AtomNet: A Deep Convolutional Neural Network for Bioactivity Prediction in Structure-based Drug Discovery
TL;DR: AtomNet is introduced, the first structure-based, deep convolutional neural network designed to predict the bioactivity of small molecules for drug discovery applications and it is shown that AtomNet outperforms previous docking approaches on a diverse set of benchmarks by a large margin.
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Prediction of protein-ligand interactions. Docking and scoring: successes and gaps.
TL;DR: The area of calculating molecular interactions, specifically docking, the positioning of a ligand in a protein binding site, and scoring, the quality assessment of docked ligands is called attention.
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Comparative Assessment of Scoring Functions: The CASF-2016 Update.
TL;DR: The latest update of this benchmark, i.e., CASF-2016, is described, which reveals that the performance of current scoring functions is more promising in terms of docking power than scoring, ranking, and screening power.
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Computer-aided drug discovery and development (CADDD): In silico-chemico-biological approach
TL;DR: It is expected that the power of CADDD will grow as the technology continues to evolve.
References
Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings
TL;DR: Experimental and computational approaches to estimate solubility and permeability in discovery and development settings are described in this article, where the rule of 5 is used to predict poor absorption or permeability when there are more than 5 H-bond donors, 10 Hbond acceptors, and the calculated Log P (CLogP) is greater than 5 (or MlogP > 415).
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Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function
Garrett M. Morris,David S. Goodsell,Robert Scott Halliday,Ruth Huey,William E. Hart,Richard K. Belew,Arthur J. Olson +6 more
TL;DR: It is shown that both the traditional and Lamarckian genetic algorithms can handle ligands with more degrees of freedom than the simulated annealing method used in earlier versions of AUTODOCK, and that the Lamarckia genetic algorithm is the most efficient, reliable, and successful of the three.
Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy.
Richard A. Friesner,Jay L. Banks,Robert B. Murphy,Thomas A. Halgren,Jasna Klicic,Daniel T. Mainz,Matthew P. Repasky,Eric H. Knoll,Mee Shelley,Jason K. Perry,David E. Shaw,Perry Francis,Peter S Shenkin +12 more
TL;DR: Glide approximates a complete systematic search of the conformational, orientational, and positional space of the docked ligand to find the best docked pose using a model energy function that combines empirical and force-field-based terms.
Development and validation of a genetic algorithm for flexible docking.
TL;DR: GOLD (Genetic Optimisation for Ligand Docking) is an automated ligand docking program that uses a genetic algorithm to explore the full range of ligand conformational flexibility with partial flexibility of the protein, and satisfies the fundamental requirement that the ligand must displace loosely bound water on binding.
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Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening.
Thomas A. Halgren,Robert B. Murphy,Richard A. Friesner,Hege S. Beard,Leah L. Frye,W. Thomas Pollard,Jay L. Banks +6 more
TL;DR: Comparisons to results for the thymidine kinase and estrogen receptors published by Rognan and co-workers show that Glide 2.5 performs better than GOLD 1.1, FlexX 1.8, or DOCK 4.01.