ArticleMolecular diversity2019
QEX: target-specific druglikeness filter enhances ligand-based virtual screening.
Article in Molecular diversity, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Role of artificial intelligence in revolutionizing drug discovery.Fundamental research · 2025Review
- Generating Potential Protein-Protein Interaction Inhibitor Molecules Based on Physicochemical Properties.Molecules (Basel, Switzerland) · 2023Article
- Quantitative Estimate Index for Early-Stage Screening of Compounds Targeting Protein-Protein Interactions.International journal of molecular sciences · 2021Article
- Artificial intelligence to deep learning: machine intelligence approach for drug discovery.Molecular diversity · 2021Review
- Logistic matrix factorisation and generative adversarial neural network-based method for predicting drug-target interactions.Molecular diversity · 2021Article
- A prospective compound screening contest identified broader inhibitors for Sirtuin 1.Scientific reports · 2019Article
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Authors and funding
5 authors.
Funding
Abstract
Druglikeness is a useful concept for screening drug candidate compounds. We developed QEX, which is a new druglikeness index specific to individual targets. QEX is an improvement of the quantitative estimate of druglikeness (QED) method, which is a popular quantitative evaluation method of druglikeness proposed by Bickerton et al. QEX models the physicochemical properties of compounds that act on each target protein based on the concept of QED modeling physicochemical properties from information on US Food and Drug Administration-approved drugs. The result of the evaluation of PubChem assay data revealed that QEX showed better performance than the original QED did (the area under the curve value of the receiver operating characteristic curve improved by 0.069-0.236). We also present the c-Src inhibitor filtering results of the QEX constructed using Src family kinase inhibitors as a case study. QEX distinguished the inhibitors and non-inhibitors better than QED did. QEX works efficiently even when datasets of inactive compounds are unavailable. If both active and inactive compounds are present, QEX can be used as an initial filter to enhance the screening ability of conventional ligand-based virtual screenings.
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