ArticleMolecular diversity2026
AI-driven generative framework integrating ML-QSAR and fragment learning for isoform-selective PI3K inhibitor design.
Article in Molecular diversity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Isoform-selective inhibition of class I phosphoinositide 3-kinases (PI3Ks) remains a major challenge in oncology and immune-mediated diseases, where dysregulated PI3K signaling drives tumor progression, therapeutic resistance, and aberrant immune activation. Efforts to achieve precise isoform selectivity are constrained by the high structural similarity among the catalytic subunits α, β, δ, and γ. In this regard, we developed an artificial intelligence (AI)-driven integrative framework that combines machine learning-based quantitative structure-activity relationship (ML-QSAR) modeling, fragment-level selectivity profiling, and reinforcement-learning generative chemistry to design isoform-selective PI3K inhibitors. Curated ChEMBL datasets were used to train independent XGBoost models for each isoform, achieving strong predictive performance (R
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