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Inductive transfer learning for molecular activity prediction: Next-Gen QSAR Models with MolPMoFiT
Deep neural networks can directly learn from chemical structures without extensive, user-driven selection of descriptors in order to predict molecular properties/activities with high reliability. But these approaches typically require large training sets to learn the endpoint-specific structural fea...
| Autores principales: | , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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Springer International Publishing
2020
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7178569/ https://www.ncbi.nlm.nih.gov/pubmed/33430978 http://dx.doi.org/10.1186/s13321-020-00430-x |