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A quantitative uncertainty metric controls error in neural network-driven chemical discovery
Machine learning (ML) models, such as artificial neural networks, have emerged as a complement to high-throughput screening, enabling characterization of new compounds in seconds instead of hours. The promise of ML models to enable large-scale chemical space exploration can only be realized if it is...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Royal Society of Chemistry
2019
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6764470/ https://www.ncbi.nlm.nih.gov/pubmed/31588334 http://dx.doi.org/10.1039/c9sc02298h |