Machine learning with physicochemical relationships: solubility prediction in organic solvents and water
| Autor: | Bao N. Nguyen, David R. J. Hose, A. John Blacker, Samuel Boobier |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Computational chemistry Computer science Science General Physics and Astronomy Process design Machine learning computer.software_genre 01 natural sciences General Biochemistry Genetics and Molecular Biology Article 03 medical and health sciences 0103 physical sciences Solubility lcsh:Science Dissolution Multidisciplinary Training set 010304 chemical physics business.industry Cheminformatics Extraction (chemistry) Computational science Statistics General Chemistry Support vector machine 030104 developmental biology Scientific method lcsh:Q Artificial intelligence business computer |
| Zdroj: | Nature Communications, Vol 11, Iss 1, Pp 1-10 (2020) Nature Communications |
| ISSN: | 2041-1723 |
| Popis: | Solubility prediction remains a critical challenge in drug development, synthetic route and chemical process design, extraction and crystallisation. Here we report a successful approach to solubility prediction in organic solvents and water using a combination of machine learning (ANN, SVM, RF, ExtraTrees, Bagging and GP) and computational chemistry. Rational interpretation of dissolution process into a numerical problem led to a small set of selected descriptors and subsequent predictions which are independent of the applied machine learning method. These models gave significantly more accurate predictions compared to benchmarked open-access and commercial tools, achieving accuracy close to the expected level of noise in training data (LogS ± 0.7). Finally, they reproduced physicochemical relationship between solubility and molecular properties in different solvents, which led to rational approaches to improve the accuracy of each models. Accurate prediction of solubility represents a challenge for traditional computational approaches due to the complex nature of phenomena involved. Here the authors report a successful approach to solubility prediction in organic solvents and water using combination of machine learning and computational chemistry. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|