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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Posted ContentDOI
Protein shape sampled by ion mobility mass spectrometry consistently improves protein structure prediction
Turzo Sba,Justin T Seffernick,Amber D. Rolland,Micah T. Donor,Heinze S,James S. Prell,Vicki H. Wysocki,Steffen Lindert +7 more
TL;DR: In this article, an integrative modelling approach was developed in Rosetta to use CCS data from ion mobility experiments, using this method, they predicted protein structures from sequence for a benchmark set of 23 proteins.
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Target Identification Using Homopharma and Network-Based Methods for Predicting Compounds Against Dengue Virus-Infected Cells.
Kowit Hengphasatporn,Kitiporn Plaimas,Apichat Suratanee,Peemapat Wongsriphisant,Jinn-Moon Yang,Yasuteru Shigeta,Warinthorn Chavasiri,Siwaporn Boonyasuppayakorn,Thanyada Rungrotmongkol +8 more
TL;DR: The DENV and human proteins obtained from this study could be potential targets for further molecular optimization on compounds with a phenolic lipid core structure in anti-dengue drug discovery and could be a valuable tool to identify possible targets of active compounds.
Journal ArticleDOI
Chemical biology and medicinal chemistry of RNA methyltransferases
Tim Fischer,Laurenz Meidner,Marvin Schwickert,Marlies Weber,Robert Zimmermann,Christian Kersten,Tanja Schirmeister,Mark Helm +7 more
TL;DR: The development of small molecules for two related aspects of chemical biology are discussed, derivates of the ubiquitous cofactor S-adenosyl-l-methionine are being developed as bioconjugation tools for targeted transfer of functional groups and labels to increasingly visible targets.
Journal ArticleDOI
Protein Function Analysis through Machine Learning
TL;DR: This work examines how ML has been integrated into a wide range of computational models to improve prediction accuracy and gain a better understanding of protein function, including protein structure prediction, protein engineering using sequence modifications to achieve stability and druggability characteristics, and protein-centric drug discovery.
Book ChapterDOI
Drug Discovery: Current State and Future Prospects
TL;DR: Innovative technologies such as combinatorial chemistry, DNA sequencing, high-throughput screening, bioinformatics, computational drug design, and computer modeling are now utilized in the drug discovery and these technologies can accelerate the success rates in introducing new molecular entities into the market.
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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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