MetaClass, a Comprehensive Classification System for Predicting the Occurrence of Metabolic Reactions Based on the MetaQSAR Database
| Autor: | Alessandro Pedretti, Alice Scaccabarozzi, Angelica Mazzolari, Giulio Vistoli |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Database
Computer science Organic Chemistry metabolic reactions Pharmaceutical Science Metaclass metabolism prediction computer.software_genre Article drug metabolism Analytical Chemistry Random forest Statistical classification MetaQSAR QD241-441 Chemistry (miscellaneous) classification algorithms Drug Discovery Molecular Medicine Physical and Theoretical Chemistry Predictability computer random forest ADME |
| Zdroj: | Molecules Volume 26 Issue 19 Molecules, Vol 26, Iss 5857, p 5857 (2021) |
| ISSN: | 1420-3049 |
| DOI: | 10.3390/molecules26195857 |
| Popis: | (1) Background: Machine learning algorithms are finding fruitful applications in predicting the ADME profile of new molecules, with a particular focus on metabolism predictions. However, the development of comprehensive metabolism predictors is hampered by the lack of highly accurate metabolic resources. Hence, we recently proposed a manually curated metabolic database (MetaQSAR), the level of accuracy of which is well suited to the development of predictive models. (2) Methods: MetaQSAR was used to extract datasets to predict the metabolic reactions subdivided into major classes, classes and subclasses. The collected datasets comprised a total of 3788 first-generation metabolic reactions. Predictive models were developed by using standard random forest algorithms and sets of physicochemical, stereo-electronic and constitutional descriptors. (3) Results: The developed models showed satisfactory performance, especially for hydrolyses and conjugations, while redox reactions were predicted with greater difficulty, which was reasonable as they depend on many complex features that are not properly encoded by the included descriptors. (4) Conclusions: The generated models allowed a precise comparison of the propensity of each metabolic reaction to be predicted and the factors affecting their predictability were discussed in detail. Overall, the study led to the development of a freely downloadable global predictor, MetaClass, which correctly predicts 80% of the reported reactions, as assessed by an explorative validation analysis on an external dataset, with an overall MCC = 0.44. |
| Databáze: | OpenAIRE |
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