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Pruned Machine Learning Models to Predict Aqueous Solubility
[Image: see text] Solubility is a key metric for therapeutic compounds. Conversely, insoluble compounds cloud the accuracy of assays at all stages of chemical biology and drug discovery. Herein, we disclose naïve Bayesian classifier models to predict aqueous solubility. Publicly accessible aqueous s...
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| Pubblicato in: | ACS Omega |
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| Autori principali: | , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
American Chemical Society
2020
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| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7364544/ https://ncbi.nlm.nih.gov/pubmed/32685821 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1021/acsomega.0c01251 |
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