ArticleMedChemComm2017
3D proteochemometrics: using three-dimensional information of proteins and ligands to address aspects of the selectivity of serine proteases.
Article in MedChemComm, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Machine-Learning-Driven Optimization of Functional Excipients and Their Biointeractions in Drug Formulations.ACS pharmacology & translational science · 2026Review
- To What Extent Can We Extrapolate Proteochemometric Models: A Case Study for the SLC6 Transporter Family.Molecular informatics · 2026Article
- How to approach machine learning-based prediction of drug/compound-target interactions.Journal of cheminformatics · 2023Article
- Application of fourier transform and proteochemometrics principles to protein engineering.BMC bioinformatics · 2018Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The high similarity between certain sub-pockets of serine proteases may lead to low selectivity of protease inhibitors. Therefore the application of proteochemometrics (PCM), which quantifies the relationship between protein/ligand descriptors and affinity for multiple ligands and targets simultaneously, is useful to understand and improve the selectivity profiles of potential inhibitors. In this study, protein field-based PCM that uses knowledge-based and WaterMap derived fields to describe proteins in combination with 2D (RDKit and MOE fingerprints) and 3D (4 point pharmacophoric fingerprints and GRIND) ligand descriptors was used to model the bioactivities of 24 homologous serine proteases and 5863 inhibitors in an integrated fashion. Of the multiple field-based PCM models generated based on different ligand descriptors, RDKit fingerprints showed the best performance in terms of external prediction with
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Registered trials
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