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Automatic design of novel potential 3CLpro and PLpro inhibitors

arXiv 2101.11890 ↗First posted 28 Jan 2021cs.LG cs.AI q-bio.QMcited by 0

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Abstract (arXiv)

With the goal of designing novel inhibitors for SARS-CoV-1 and SARS-CoV-2, we propose the general molecule optimization framework, Molecular Neural Assay Search (MONAS), consisting of three components: a property predictor which identifies molecules with specific desirable properties, an energy model which approximates the statistical similarity of a given molecule to known training molecules, and a molecule search method. In this work, these components are instantiated with graph neural networks (GNNs), Deep Energy Estimator Networks (DEEN) and Monte Carlo tree search (MCTS), respectively. This implementation is used to identify 120K molecules (out of 40-million explored) which the GNN determined to be likely SARS-CoV-1 inhibitors, and, at the same time, are statistically close to the dataset used to train the GNN.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 61 references that are in the map. First posted on arXiv 28 Jan 2021.

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References · 26

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14 more
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