ArticleFrontiers in chemistry2022
Comparison of Descriptor- and Fingerprint Sets in Machine Learning Models for ADME-Tox Targets.
Article in Frontiers in chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 66 citations in OpenAlex.
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- Development of machine learning-based multi-task quantitative structure-activity relationship models for predicting toxicities in six human organ systems.Computational toxicology (Amsterdam, Netherlands) · 2026Article
- Discovering TEAD4 modulators for hepatocellular carcinoma: a GAN-enabled generative modelling framework.Frontiers in bioinformatics · 2026Article
- Artificial Intelligence and Machine Learning-Based Approaches for Genetic Damage Prediction.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Enhancing cancer drug discovery: QSAR modeling with machine learning and chemical representations.PloS one · 2026Article
- Mining Structural Information from Gas Chromatography-Electron-Impact Ionization-Mass Spectrometry Data for Analytical-Descriptor-Based Quantitative Structure-Activity Relationship.Journal of xenobiotics · 2025Article
- Analytical and machine learning approaches identify a sea star steroid with promising activity for COVID-19 therapeutic development.Scientific reports · 2025Article
- ADMET evaluation in drug discovery: 21. Application and industrial validation of machine learning algorithms for Caco-2 permeability prediction.Journal of cheminformatics · 2025Article
- Machine learning prediction of intestinal α-glucosidase inhibitors using a diverse set of ligands: a drug repurposing effort with drugBank database screening.In silico pharmacology · 2025Article
- Deepmol: an automated machine and deep learning framework for computational chemistry.Journal of cheminformatics · 2024Article
- Three-Dimensional Quantitative Structure-Activity Relationship Study of Transient Receptor Potential Vanilloid 1 Channel Antagonists Reveals Potential for Drug Design Purposes.International journal of molecular sciences · 2024Article
- Systematic Approaches for the Encoding of Chemical Groups: A Case Study.Chemical research in toxicology · 2024Article
- Machine Learning-Based Analysis Reveals Triterpene Saponins and Their Aglycones inPharmaceutics · 2024Article
- Bayesian Optimization in Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Collaborative SAR Modeling and Prospective In Vitro Validation of Oxidative Stress Activation in Human HepG2 Cells.Journal of chemical information and modeling · 2023Article
- Evaluating the Use of Graph Neural Networks and Transfer Learning for Oral Bioavailability Prediction.Journal of chemical information and modeling · 2023Article
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- Redesigning plant specialized metabolism with supervised machine learning using publicly available reactome data.Computational and structural biotechnology journal · 2023Review
- Cheminformatics and artificial intelligence for accelerating agrochemical discovery.Frontiers in chemistry · 2023Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The screening of compounds for ADME-Tox targets plays an important role in drug design. QSPR models can increase the speed of these specific tasks, although the performance of the models highly depends on several factors, such as the applied molecular descriptors. In this study, a detailed comparison of the most popular descriptor groups has been carried out for six main ADME-Tox classification targets: Ames mutagenicity, P-glycoprotein inhibition, hERG inhibition, hepatotoxicity, blood-brain-barrier permeability, and cytochrome P450 2C9 inhibition. The literature-based, medium-sized binary classification datasets (all above 1,000 molecules) were used for the model building by two common algorithms, XGBoost and the RPropMLP neural network. Five molecular representation sets were compared along with their joint applications: Morgan, Atompairs, and MACCS fingerprints, and the traditional 1D and 2D molecular descriptors, as well as 3D molecular descriptors, separately. The statistical evaluation of the model performances was based on 18 different performance parameters. Although all the developed models were close to the usual performance of QSPR models for each specific ADME-Tox target, the results clearly showed the superiority of the traditional 1D, 2D, and 3D descriptors in the case of the XGBoost algorithm. It is worth trying the classical tools in single model building because the use of 2D descriptors can produce even better models for almost every dataset than the combination of all the examined descriptor sets.
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