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Pu2019 - eToxPred: an ML-based approach to estimate the toxicity, and synthetic accessibility of drug candidates

ABSTRACT: The eToxPred tool has been developed to predict, on one hand, the synthetic accessibility (SA) score, or how easy it is to make the molecule in the laboratory, and, on the other hand, the toxicity (Tox) score, or the probability of the molecule of being toxic to humans. The authors trained and cross-validated both predictors on a large number of datasets, and demonstrated the method usefulness in building virtual custom libraries. Model Type: Predicitive machine learning model. Model Relevance: Predicts Synthetic Accesibility and Toxicity score of a chemical compound Model Encoded by: Miquel Duran-Frigola (Ersilia) Metadata Submitted in BioModels by: Zainab Ashimiyu-Abdusalam Implementation of this model code by Ersilia is available here: https://github.com/ersilia-os/eos92sw

SUBMITTER: Zainab Ashimiyu-Abdusalam

PROVIDER: MODEL2406270007 | BioModels | 2024-07-18

REPOSITORIES: BioModels

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eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates.

Pu Limeng L Naderi Misagh M Liu Tairan T Wu Hsiao-Chun HC Mukhopadhyay Supratik S Brylinski Michal M

BMC pharmacology & toxicology 20190108 1

<h4>Background</h4>The efficiency of drug development defined as a number of successfully launched new pharmaceuticals normalized by financial investments has significantly declined. Nonetheless, recent advances in high-throughput experimental techniques and computational modeling promise reductions in the costs and development times required to bring new drugs to market. The prediction of toxicity of drug candidates is one of the important components of modern drug discovery.<h4>Results</h4>In ...[more]

PMID: 30621790

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