ReviewMolecular diversity2021
Machine learning models for classification tasks related to drug safety.
Review in Molecular diversity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 53 citations in OpenAlex.
- L-Type Voltage-Gated CaMedical sciences (Basel, Switzerland) · 2026Article
- In Silico Forensic Toxicology: Is It Feasible?Toxics · 2025Review
- Prioritisation of co-formulants and plant protection products for non-dietary risk assessment using NAMs.Archives of toxicology · 2025Article
- Real-Time Monitoring of the Antiseizure Drug Valproic Acid Using a Novel Point-Of-Care Mass Spectrometry.CNS neuroscience & therapeutics · 2025Article
- Novel target identification towards drug repurposing based on biological activity profiles.PloS one · 2025Article
- AttenhERG: a reliable and interpretable graph neural network framework for predicting hERG channel blockers.Journal of cheminformatics · 2024Article
- Alternative weighting schemes for fine-tuned extended similarity indices.Journal of chemometrics · 2024Article
- Predicting blood-brain barrier permeability of molecules with a large language model and machine learning.Scientific reports · 2024Article
- A Review of CYP-Mediated Drug Interactions: Mechanisms and In Vitro Drug-Drug Interaction Assessment.Biomolecules · 2024Review
- ProfhEX: AI-based platform for small molecules liability profiling.Journal of cheminformatics · 2023Article
- Machine Learning for Ionization Potentials and Photoionization Cross Sections of Volatile Organic Compounds.ACS earth & space chemistry · 2023Article
- Prediction of the Contribution Ratio of a Target Metabolic Enzyme to Clearance from Chemical Structure Information.Molecular pharmaceutics · 2023Article
- Physicochemical QSAR analysis of hERG inhibition revisited: towards a quantitative potency prediction.Journal of computer-aided molecular design · 2022Article
- Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches.Current opinion in chemical biology · 2022Review
- Molecular Dynamics Simulations and Diversity Selection by Extended Continuous Similarity Indices.Journal of chemical information and modeling · 2022Article
- Quantitative Retention (Structure)-Activity Relationships in Predicting the Pharmaceutical and Toxic Properties of Potential Pesticides.Molecules (Basel, Switzerland) · 2022Article
- An Explainable Supervised Machine Learning Model for Predicting Respiratory Toxicity of Chemicals Using Optimal Molecular Descriptors.Pharmaceutics · 2022Article
- Extended continuous similarity indices: theory and application for QSAR descriptor selection.Journal of computer-aided molecular design · 2022Article
- Comparison of Descriptor- and Fingerprint Sets in Machine Learning Models for ADME-Tox Targets.Frontiers in chemistry · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study focuses only on classification models with large datasets (i.e. more than a thousand compounds). A comprehensive literature search and meta-analysis was carried out for nine different targets: hERG-mediated cardiotoxicity, blood-brain barrier penetration, permeability glycoprotein (P-gp) substrate/inhibitor, cytochrome P450 enzyme family, acute oral toxicity, mutagenicity, carcinogenicity, respiratory toxicity and irritation/corrosion. The comparison of the best classification models was targeted to reveal the differences between machine learning algorithms and modeling types, endpoint-specific performances, dataset sizes and the different validation protocols. Based on the evaluation of the data, we can say that tree-based algorithms are (still) dominating the field, with consensus modeling being an increasing trend in drug safety predictions. Although one can already find classification models with great performances to hERG-mediated cardiotoxicity and the isoenzymes of the cytochrome P450 enzyme family, these targets are still central to ADMET-related research efforts.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.