Computational Methods in Drug Discovery

By Gregory Sliwoski, Sandeepkumar Kothiwale, Jens Meiler, E. W. Lowe. 2014

1,984 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Organic Chemistry, Pharmacology, Computational Chemistry & Drug Discovery

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In Computational Theory and Mathematics

In Organic Chemistry

In Pharmacology

In Computational Chemistry & Drug Discovery

Citations in the corpus

Molecular Docking and Structure-Based Drug Design Strategies (2015, background): “LBVS is based on the exploration of molecular descriptors gathered from compounds known to be active [129,131].”
Molecular docking as a tool for the discovery of molecular targets of nutraceuticals in diseases management (2023, methodology): “The technique involves the simulation of the molecular interactions between the small molecule and the target biomolecule, which can provide insights into the binding affinity, binding site, and possible mechanism of action 83 .”
Machine Learning in Drug Discovery: A Review (2021, methodology): “In this concept, the aim is to persistently improve yields and to decrease false hit rates (Leelananda and Lindert 2016; Liew et al. 2009; Melville et al. 2009).”
Potential of quantum computing for drug discovery (2018, methodology): “This information is usually obtained experimentally using nuclear magnetic resonance (NMR) spectroscopy and X-ray crystallography studies on crystallized protein [4].”
Constrained Bayesian Optimization for Automatic Chemical Design (2017, background): “ search methods in de novo drug design such as genetic algorithms cannot make use of continuous optimization techniques which may leverage geometrical cues such as gradients to optimize the objective [99, 107]. Recently however, advances in machine learning and in particular in the field of deep gen”
Computer-Aided Drug Design Methods (2017, methodology): “Notably, CADD methods are evolving with researchers continually updating and implementing new CADD techniques with higher levels of accuracy and speed (24–26).”
Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases (2018, background): “Structure-based VS methods are out of the scope of this study, and information about this field can be obtained from the literature [31, 32, 52, 56, 57].”