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Accelerating high-throughput virtual screening through molecular pool-based active learning
Structure-based virtual screening is an important tool in early stage dr...
12/13/2020 ∙ by David E. Graff, et al. ∙
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Learning Graph Models for Template-Free Retrosynthesis
Retrosynthesis prediction is a fundamental problem in organic synthesis,...
06/12/2020 ∙ by Vignesh Ram Somnath, et al. ∙
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Uncertainty Quantification Using Neural Networks for Molecular Property Prediction
Uncertainty quantification (UQ) is an important component of molecular p...
05/20/2020 ∙ by Lior Hirschfeld, et al. ∙
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Learning To Navigate The Synthetically Accessible Chemical Space Using Reinforcement Learning
Over the last decade, there has been significant progress in the field o...
04/26/2020 ∙ by Sai Krishna Gottipati, et al. ∙
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Autonomous discovery in the chemical sciences part II: Outlook
This two-part review examines how automation has contributed to differen...
03/30/2020 ∙ by Connor W. Coley, et al. ∙
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Autonomous discovery in the chemical sciences part I: Progress
This two-part review examines how automation has contributed to differen...
03/30/2020 ∙ by Connor W. Coley, et al. ∙
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The Synthesizability of Molecules Proposed by Generative Models
The discovery of functional molecules is an expensive and time-consuming...
02/17/2020 ∙ by Wenhao Gao, et al. ∙
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Retrosynthesis Prediction with Conditional Graph Logic Network
Retrosynthesis is one of the fundamental problems in organic chemistry. ...
01/06/2020 ∙ by Hanjun Dai, et al. ∙
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Learning retrosynthetic planning through self-play
The problem of retrosynthetic planning can be framed as one player game,...
01/19/2019 ∙ by John S. Schreck, et al. ∙
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Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network
The prediction of organic reaction outcomes is a fundamental problem in ...
09/13/2017 ∙ by Wengong Jin, et al. ∙
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