What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery

By Edward O. Pyzer-Knapp, Changwon Suh, Rafael Gómez‐Bombarelli, Jorge Aguilera-Iparraguirre, Alán Aspuru-Guzik. 2015

286 citations recorded by the source corpus.

Subfields: Catalysis, Computational Theory and Mathematics, Materials Chemistry

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Citations in the corpus

Constrained Bayesian Optimization for Automatic Chemical Design (2017, methodology): “The use of geometrical cues such as gradients to guide the search in continuous space could accelerate both drug [Pyzer-Knapp et al., 2015, Gómez-Bombarelli et al., 2016a] and material [Hachmann et al., 2011, 2014] discovery by functioning as a high-throughput virtual screen of unpromising…”
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models (2018, methodology): “There have been many approaches to mapping and mining the space of possible molecules, including high throughput screening, combinatorial libraries, evolutionary algorithms, and other techniques [5–8].”
Progress in molecular docking (2019, unknown): “[19] proposed the virtual high-throughput screening.”
From Variational to Deterministic Autoencoders (2019, background): “some ground truth points, and the water-octanol partition coefficient (logP ) (Pyzer-Knapp et al., 2015) (higher is better) in the case of molecules.”
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space (2017, background): “These have led to the development of high-throughput virtual screening (Pyzer-Knapp et al., 2015; G ´ omez-Bombarelli et al., 2016) in which large libraries of molecules are created and then analyzed using theoretical and computational techniques, typically by running a large number of parallel…”
In silico evaluation of highly efficient organic light-emitting materials (2016, background): “Such toolsets can enable more focused investigations over a smaller chemical space, either with high-level theoretical investigations or further experimental screening [2].”
All SMILES Variational Autoencoder (2019, background): “The design of new pharmaceuticals, OLED materials, and photovoltaics all require optimization within the space of molecules [50].”