Article

Machine Learning Models to Interrogate Proteomewide Covalent Ligandabilities Directed at Cysteines

2023-08-21

Abstract excerpt

Machine learning (ML) identification of covalently ligandable sites may accelerate targeted covalent inhibitor design and help expand the druggable proteome space. Here we report the rigorous development and validation of the tree-based models and convolutional neural networks (CNNs) trained on a newly curated database (LigCys3D) of over 1,000 liganded cysteines in nearly 800 proteins represented by over 10,000 th...

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Literature Corpus work: 2387ca3f-ebac-57d5-8399-4cad573e4e3d
DOI: 10.1101/2023.08.17.553742

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