Virtual screening of chemical libraries

By Brian K. Shoichet. 2004

1,355 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Massively Multitask Networks for Drug Discovery (2015, methodology): “Virtual screening attempts to replace or augment the high-throughput screening process by the use of computational methods (Shoichet, 2004).”
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules (2016, neutral): “− 6 Virtual libraries containing thousands to hundreds of millions of candidates can be assayed with ﬁ rst-principles simulations or statistical predictions based on learned proxy models, and only the most promising leads are selected and tested experimentally.”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, neutral): “Accurate and generalizable molecular property predictors are useful for virtual screening (Shoichet, 2004), i.e., computer-aided searches over huge collections of small molecules in order to identify the structures which are most likely to bind to a drug target.”
Cloud-based simulations on Google Exacycle reveal ligand-modulation of GPCR activation pathways (2013, background): “Next, we show that docking to MSM states expands the chemical space of our docking results, an essential advantage in docking approaches(25).”
Molecular dynamics simulation for all (2018, neutral): “Traditional virtual screening is performed with docking software using a single structure of a target protein (Shoichet, 2004).”
Computer-aided drug discovery and development (CADDD): in silico-chemico-biological approach. (2008, background): “Shoichet reported that ligand discovery i.e. hit rates (number of compounds binding to a target divided by number of compounds tested) is greater in virtual screening by 2 or 3 orders of magnitude than in empirical screening [40].”
Enhanced Deep‐Learning Prediction of Molecular Properties via Augmentation of Bond Topology (2018, methodology): “Chemists have developed various methods for computationally simulating these interactions with or without information on the actual structure of the binding pocket, based on physicochemical calculations and chemical-similarity analyses.(4,5) Besides the ongoing efforts on the computational-chemistry”
DeepDock: Enhancing Ligand-protein Interaction Prediction by a Combination of Ligand and Structure Information (2019, background): “This search process is known as virtual screening (VS) [2].”
In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling (2007, methodology): “Target-based virtual screening methods depend on the availability of structural information of the target, that being either determined experimentally or derived computationally by means of homology modelling techniques (Shoichet, 2004; Klebe, 2006).”
Virtual Screening with AutoDock: Theory and Practice (2010, background): “Today, virtual screening is widely used to predict the binding of a large database of ligands to a particular target, with the goal of identifying the most promising compounds from the database for further study [10,14-20].”