ArticleJournal of cheminformatics2021
Prediction of activity and selectivity profiles of human Carbonic Anhydrase inhibitors using machine learning classification models.
Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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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.
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Who cites it
15 citing papers in PubMed, 31 citations in OpenAlex.
- Explainable Multi-Isoform QSAR, PubChem Concordance, and Applicability-Domain-Guided Prioritization of Selective Human Carbonic Anhydrase I, II, IX, and XII Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Explainable Artificial Intelligence (XAI) and Molecular Modeling Techniques to Discover Putative HER2 Inhibitors.International journal of molecular sciences · 2026Article
- Carbonic anhydrase under the computational lens: a review of advances, challenges, and future directions.Journal of molecular modeling · 2026Review
- Interpretable machine learning rationalizes carbonic anhydrase inhibition via conformal and counterfactual prediction.Scientific reports · 2026Article
- Computational Approaches and Structure-Based Drug Design of CAIs.Sub-cellular biochemistry · 2026Review
- Prediction of Activity and Selectivity Profiles of Sigma Receptor Ligands Using Machine Learning Approaches.Journal of chemical information and modeling · 2025Article
- A Machine Learning Platform for Isoform-Specific Identification and Profiling of Human Carbonic Anhydrase Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Novel 3-Sulfonamide Dual-Tail Pyrrol-2-one Bridged Molecules as Potent Human Carbonic Anhydrase Isoform Inhibitors: Design, Synthesis, Molecular Modeling Investigation, and Anticancer Activity in MeWo, SK-BR-3, and MG-63 Cell Lines.Journal of medicinal chemistry · 2025Article
- Machine Learning-Driven Classification of Urease Inhibitors Leveraging Physicochemical Properties as Effective Filter Criteria.International journal of molecular sciences · 2024Article
- FP-MAP: an extensive library of fingerprint-based molecular activity prediction tools.Frontiers in chemistry · 2023Article
- A journey from molecule to physiology andFrontiers in pharmacology · 2023Review
- Bio-activity prediction of drug candidate compounds targeting SARS-Cov-2 using machine learning approaches.PloS one · 2023Article
- Development of machine learning classifiers to predict compound activity on prostate cancer cell lines.Journal of cheminformatics · 2022Article
- Bis-pharmacophore of cinnamaldehyde-clubbed thiosemicarbazones as potent carbonic anhydrase-II inhibitors.Scientific reports · 2022Article
- Machine Learning Approaches for Metalloproteins.Molecules (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
The development of selective inhibitors of the clinically relevant human Carbonic Anhydrase (hCA) isoforms IX and XII has become a major topic in drug research, due to their deregulation in several types of cancer. Indeed, the selective inhibition of these two isoforms, especially with respect to the homeostatic isoform II, holds great promise to develop anticancer drugs with limited side effects. Therefore, the development of in silico models able to predict the activity and selectivity against the desired isoform(s) is of central interest. In this work, we have developed a series of machine learning classification models, trained on high confidence data extracted from ChEMBL, able to predict the activity and selectivity profiles of ligands for human Carbonic Anhydrase isoforms II, IX and XII. The training datasets were built with a procedure that made use of flexible bioactivity thresholds to obtain well-balanced active and inactive classes. We used multiple algorithms and sampling sizes to finally select activity models able to classify active or inactive molecules with excellent performances. Remarkably, the results herein reported turned out to be better than those obtained by models built with the classic approach of selecting an a priori activity threshold. The sequential application of such validated models enables virtual screening to be performed in a fast and more reliable way to predict the activity and selectivity profiles against the investigated isoforms.
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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.