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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Applications and limitations of in silico models in drug discovery.
TL;DR: This chapter highlights the applications of in silico strategies for lead design and optimization that perform complementary roles to that of the traditional in vitro and in vivo approaches.
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Cameroonian medicinal plants as potential candidates of SARS-CoV-2 inhibitors.
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Emerging Screening Approaches in the Development of Nrf2–Keap1 Protein–Protein Interaction Inhibitors
TL;DR: Virtual screening and other methods for discovering new lead compounds against the Keap1–Nrf2 protein–protein interaction are summarized and the potential of this PPI as a drug target in disease therapy is discussed.
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The Development of Target-Specific Machine Learning Models as Scoring Functions for Docking-Based Target Prediction.
Mauro Oddo Nogueira,Oliver Koch +1 more
TL;DR: A development of target-specific scoring functions is described that showed improved prediction performances for the correct target prediction of both actives and decoys on three validation data sets.
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Active compounds activity from the medicinal plants against SARS-CoV-2 using in silico assay
Ni Putu Linda Laksmiani,Luh Putu Febryana Larasanty,Anak Agung Gde Jaya Santika,Putu Agus Andika Prayoga,Anak Agung Intan Kharisma Dewi,Ni Putu Ayu Kristiara Dewi +5 more
TL;DR: It reflects that active compounds in medicinal plants can be used as antiviral against COVID-19, and inhibiting ACE2, TMPRSS2, RdRp and protease interfered the process of virus infection at the entry, replication and advanced stages, causing worst effect such as pneumonia.
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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.