Assessment of machine learning approaches for predicting the crystallization propensity of active pharmaceutical ingredients
| Autor: | Joseph F. Krzyzaniak, Lydie Louis, Bruno C. Hancock, Paul Meenan, Kapildev K. Arora, Ayana Ghosh, Serge Nakhmanson, Geoffrey P. F. Wood |
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| Rok vydání: | 2019 |
| Předmět: |
Mean squared error
02 engineering and technology 010402 general chemistry Machine learning computer.software_genre 01 natural sciences law.invention Set (abstract data type) law Molecular descriptor General Materials Science Crystallization Mathematics Active ingredient Artificial neural network business.industry General Chemistry 021001 nanoscience & nanotechnology Condensed Matter Physics 0104 chemical sciences Random forest Solvent models Artificial intelligence 0210 nano-technology business computer |
| Zdroj: | CrystEngComm. 21:1215-1223 |
| ISSN: | 1466-8033 |
| DOI: | 10.1039/c8ce01589a |
| Popis: | In the current report, three machine learning approaches were assessed for their ability to predict the crystallization propensities of a set of small organic compounds ( |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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