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VenomPred: A Machine Learning Based Platform for Molecular Toxicity Predictions.
- Source :
-
International journal of molecular sciences [Int J Mol Sci] 2022 Feb 14; Vol. 23 (4). Date of Electronic Publication: 2022 Feb 14. - Publication Year :
- 2022
-
Abstract
- The use of in silico toxicity prediction methods plays an important role in the selection of lead compounds and in ADMET studies since in vitro and in vivo methods are often limited by ethics, time, budget and other resources. In this context, we present our new web tool VenomPred, a user-friendly platform for evaluating the potential mutagenic, hepatotoxic, carcinogenic and estrogenic effects of small molecules. VenomPred platform employs several in-house Machine Learning (ML) models developed with datasets derived from VEGA QSAR, a software that includes a comprehensive collection of different toxicity models and has been used as a reference for building and evaluating our ML models. The results showed that our models achieved equal or better performance than those obtained with the reference models included in VEGA QSAR. In order to improve the predictive performance of our platform, we adopted a consensus approach combining the results of different ML models, which was able to predict chemical toxicity better than the single models. This improved method was thus implemented in the VenomPred platform, a freely accessible webserver that takes the SMILES (Simplified Molecular-Input Line-Entry System) strings of the compounds as input and sends the prediction results providing a probability score about their potential toxicity.
- Subjects :
- Computer Simulation
Machine Learning
Mutagenesis drug effects
Quantitative Structure-Activity Relationship
Software
Carcinogens toxicity
Drug-Related Side Effects and Adverse Reactions prevention & control
Mutagens adverse effects
Small Molecule Libraries adverse effects
Small Molecule Libraries chemistry
Subjects
Details
- Language :
- English
- ISSN :
- 1422-0067
- Volume :
- 23
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- International journal of molecular sciences
- Publication Type :
- Academic Journal
- Accession number :
- 35216217
- Full Text :
- https://doi.org/10.3390/ijms23042105