ArticleMolecular diversity2021
A multi-conformational virtual screening approach based on machine learning targeting PI3Kγ.
Article in Molecular diversity, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 17 citations in OpenAlex.
- Effective virtual screening strategy toward JAK3 covalent inhibitors: combining multi‑conformational consensus calculation with covalent docking.Molecular diversity · 2026Article
- Artificial intelligence for precision oncology from phenotyping and drug discovery to clinical translation.Discover oncology · 2026Review
- Discovery of Putative GyrB Inhibitors againstCurrent medicinal chemistry · 2026Article
- Identification of potential PI3Kγ inhibitors among FDA approved drugs using integrated computational and quantum chemical approaches.Scientific reports · 2025Article
- Enhancing PI3Kγ inhibitor discovery: a machine learning-based virtual screening approach integrating pharmacophores, docking, and molecular descriptors.Molecular diversity · 2025Article
- Identification of novel covalent JAK3 inhibitors through consensus scoring virtual screening: integration of common feature pharmacophore and covalent docking.Molecular diversity · 2025Article
- Identifying Natural Products as Feline Coronavirus MACS omega · 2025Article
- Discovery of novel S6K1 inhibitors by an ensemble-based virtual screening method and molecular dynamics simulation.Journal of molecular modeling · 2023Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Nowadays, more and more attention has been attracted to develop selective PI3Kγ inhibitors, but the unique structural features of PI3Kγ protein make it a very big challenge. In the present study, a virtual screening strategy based on machine learning with multiple PI3Kγ protein structures was developed to screen novel PI3Kγ inhibitors. First, six mainstream docking programs were chosen to evaluate their scoring power and screening power; CDOCKER and Glide show satisfactory reliability and accuracy against the PI3Kγ system. Next, virtual screening integrating multiple PI3Kγ protein structures was demonstrated to significantly improve the screening enrichment rate comparing to that with an individual protein structure. Last, a multi-conformational Naïve Bayesian Classification model with the optimal docking programs was constructed, and it performed a true capability in the screening of PI3Kγ inhibitors. Taken together, the current study could provide some guidance for the docking-based virtual screening to discover novel PI3Kγ inhibitors.
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Registered trials
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