ArticleInternational journal of molecular sciences2022
VenomPred: A Machine Learning Based Platform for Molecular Toxicity Predictions.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Article
- Perspective on applicability of data-driven machine learning computational new approach methodologies for hazard identification in chemicals risk assessment.Journal of cheminformatics · 2026Review
- Artificial Intelligence and Machine Learning-Based Approaches for Genetic Damage Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- KinasePred: A Computational Tool for Small-Molecule Kinase Target Prediction.International journal of molecular sciences · 2025Article
- Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals.Journal of environmental science and health. Part C, Toxicology and carcinogenesis · 2025Review
- Deep active learning with high structural discriminability for molecular mutagenicity prediction.Communications biology · 2024Article
- VenomPred 2.0: A NovelJournal of chemical information and modeling · 2024Article
- Identification of New GSK3β Inhibitors through a Consensus Machine Learning-Based Virtual Screening.International journal of molecular sciences · 2023Article
- An in silico toolbox for the prediction of the potential pathogenic effects of missense mutations in the dimeric region ofJournal of enzyme inhibition and medicinal chemistry · 2023Article
- Nature-Inspired Compounds: Synthesis and Antibacterial Susceptibility Testing of Eugenol Derivatives againstPharmaceuticals (Basel, Switzerland) · 2023Article
- MolBook UNIPI─Create, Manage, Analyze, and Share Your Chemical Data for Free.Journal of chemical information and modeling · 2023Article
- Rhamnopyranoside-Based Fatty Acid Esters as Antimicrobials: Synthesis, Spectral Characterization, PASS, Antimicrobial, and Molecular Docking Studies.Molecules (Basel, Switzerland) · 2023Article
- Machine Learning-Based Virtual Screening for the Identification of Cdk5 Inhibitors.International journal of molecular sciences · 2022Article
- Structural and Biofunctional Insights into the Cyclo(Pro-Pro-Phe-Phe-) Scaffold from Experimental and In Silico Studies: Melanoma and Beyond.International journal of molecular sciences · 2022Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.