ArticleInternational journal of molecular sciences2018
Development of a Fingerprint-Based Scoring Function for the Prediction of the Binding Mode of Carbonic Anhydrase II Inhibitors.
Article in International journal of molecular sciences, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 14 citations in OpenAlex.
- A Machine Learning Platform for Isoform-Specific Identification and Profiling of Human Carbonic Anhydrase Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Prediction of activity and selectivity profiles of human Carbonic Anhydrase inhibitors using machine learning classification models.Journal of cheminformatics · 2021Article
- Development of a cheminformatics platform for selectivity analyses of carbonic anhydrase inhibitors.Journal of enzyme inhibition and medicinal chemistry · 2020Article
- Application of MM-PBSA Methods in Virtual Screening.Molecules (Basel, Switzerland) · 2020Review
- Computationally driven discovery of phenyl(piperazin-1-yl)methanone derivatives as reversible monoacylglycerol lipase (MAGL) inhibitors.Journal of enzyme inhibition and medicinal chemistry · 2019Article
- Novel 8-Substituted Coumarins That Selectively Inhibit Human Carbonic Anhydrase IX and XII.International journal of molecular sciences · 2019Article
- Target-based drug discovery through inversion of quantitative structure-drug-property relationships and molecular simulation: CA IX-sulphonamide complexes.Journal of enzyme inhibition and medicinal chemistry · 2018Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Carbonic anhydrase II (CAII) is a zinc-containing metalloenzyme whose aberrant activity is associated with various diseases such as glaucoma, osteoporosis, and different types of tumors; therefore, the development of CAII inhibitors, which can represent promising therapeutic agents for the treatment of these pathologies, is a current topic in medicinal chemistry. Molecular docking is a commonly used tool in structure-based drug design of enzyme inhibitors. However, there is still a need for improving docking reliability, especially in terms of scoring functions, since the complex pattern of energetic contributions driving ligand⁻protein binding cannot be properly described by mathematical functions only including approximated energetic terms. Here we report a novel CAII-specific fingerprint-based (IFP) scoring function developed according to the ligand⁻protein interactions detected in the CAII-inhibitor co-crystal structures of the most potent CAII ligands. Our IFP scoring function outperformed the ability of Autodock4 scoring function to identify native-like docking poses of CAII inhibitors and thus allowed a considerable improvement of docking reliability. Moreover, the ligand⁻protein interaction fingerprints showed a useful application in the binding mode analysis of structurally diverse CAII ligands.
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