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Machine Learning Models to Interrogate Proteome-wide Cysteine Ligandabilities
Machine learning (ML) identification of covalently ligandable sites may significantly accelerate targeted covalent inhibitor discoveries and expand the druggable proteome space. Here we report the development of the tree-based models and convolutional neural networks trained on a newly curated datab...
| Autores principales: | , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Cold Spring Harbor Laboratory
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473668/ https://www.ncbi.nlm.nih.gov/pubmed/37662346 http://dx.doi.org/10.1101/2023.08.17.553742 |