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Explaining compound activity predictions with a substructure-aware loss for graph neural networks
Explainable machine learning is increasingly used in drug discovery to help rationalize compound property predictions. Feature attribution techniques are popular choices to identify which molecular substructures are responsible for a predicted property change. However, established molecular feature...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Springer International Publishing
2023
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10369817/ https://www.ncbi.nlm.nih.gov/pubmed/37491407 http://dx.doi.org/10.1186/s13321-023-00733-9 |