Benefit of Retraining pKa Models Studied Using Internally Measured Data
| Autor: | Weiping Jia, Stephane Rodde, Riccardo Vianello, Gavin Dollinger, Peter Gedeck, Franco Lombardo, Suzanne Skolnik, Bernard Faller, Yipin Lu, Giuliano Berellini |
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| Rok vydání: | 2015 |
| Předmět: |
Chemical Phenomena
Computer science Process (engineering) business.industry General Chemical Engineering Mean squared prediction error Retraining Structure property Reproducibility of Results General Chemistry Library and Information Sciences Models Theoretical Machine learning computer.software_genre Chemical space Computer Science Applications Machine Learning Software Artificial intelligence business computer Simulation |
| Zdroj: | Journal of chemical information and modeling. 55(7) |
| ISSN: | 1549-960X |
| Popis: | The ionization state of drugs influences many pharmaceutical properties such as their solubility, permeability, and biological activity. It is therefore important to understand the structure property relationship for the acid-base dissociation constant pKa during the lead optimization process to make better-informed design decisions. Computational approaches, such as implemented in MoKa, can help with this; however, they often predict with too large error especially for proprietary compounds. In this contribution, we look at how retraining helps to greatly improve prediction error. Using a longitudinal study with data measured over 15 years in a drug discovery environment, we assess the impact of model training on prediction accuracy and look at model degradation over time. Using the MoKa software, we will demonstrate that regular retraining is required to address changes in chemical space leading to model degradation over six to nine months. |
| Databáze: | OpenAIRE |
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