ArticleFrontiers in pharmacology2017
ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches.
Article in Frontiers in pharmacology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Language of Toxicity: An eXplainable Artificial Intelligence Approach.Journal of chemical information and modeling · 2026Article
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- Artificial Intelligence and Machine Learning-Based Approaches for Genetic Damage Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- From molecules to data: the emerging impact of chemoinformatics in chemistry.Journal of cheminformatics · 2025Review
- A mixture parameterized biologically based dosimetry model to predict body burdens of polycyclic aromatic hydrocarbons in developmental zebrafish toxicity assays.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- GutBugDB: a web resource to predict the human gut microbiome-mediated biotransformation of biotic and xenobiotic molecules.Gut microbiome (Cambridge, England) · 2025Article
- Toxicological Evaluation of Kaempferol and Linearolactone as Treatments for Amoebic Liver Abscess Development inInternational journal of molecular sciences · 2024Article
- Article
- Clustering Protein Binding Pockets and Identifying Potential Drug Interactions: A Novel Ligand-Based Featurization Method.Journal of chemical information and modeling · 2023Article
- Accurate clinical toxicity prediction using multi-task deep neural nets and contrastive molecular explanations.Scientific reports · 2023Article
- Application of artificial intelligence approaches to predict the metabolism of xenobiotic molecules by human gut microbiome.Frontiers in microbiology · 2023Review
- Predicting Dose-Range Chemical Toxicity using Novel Hybrid Deep Machine-Learning Method.Toxics · 2022Article
- Discovery of Potential SARS-CoV-2 Papain-like Protease Natural Inhibitors Employing a Multi-Phase In Silico Approach.Life (Basel, Switzerland) · 2022Article
- Mechanistic Insights into Passive Membrane Permeability of Drug-like Molecules from a Weighted Ensemble of Trajectories.Journal of chemical information and modeling · 2022Article
- In silico approach for identifying natural lead molecules against SARS-COV-2.Journal of molecular graphics & modelling · 2021Article
- Data science-driven analyses of drugs inducing hypertension as an adverse effect.Molecular diversity · 2021Article
- Artificial Intelligence in Functional Food Ingredient Discovery and Characterisation: A Focus on Bioactive Plant and Food Peptides.Frontiers in genetics · 2021Review
- Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis.International journal of molecular sciences · 2018Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84-0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better (
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