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February 5, 2026Artificial Intelligence ChemistryOpen Access

A computational pipeline combining machine learning and molecular simulations identifies repurposed PI3Kα inhibitors from FDA-approved drugs

Victoria Ohene-Adu Naomi Kayeri Seth Junior Ayihi Syed Arslan Haider Hope Dzitorwoko Kwabena Adu-Adjei Michael Osei Kwabena Adu-Adjei Portia Frimpong Clifford Tignangnen Penajah Silas Boakye Amissah Nimra Mazhar Cedric Dzidzor Kodjo Amengor

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Key Points

Repurposed PI3Kα inhibitors were identified through a computational pipeline, enhancing drug discovery.
Machine learning methods revealed potential candidates among FDA-approved drugs, improving efficiency.
Observational analysis used molecular simulations to evaluate the efficacy of identified inhibitors against PI3Kα.
Findings suggest that leveraging existing drugs may expedite treatment options for specific conditions.

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Implication

Analysis combines machine learning and molecular simulations to identify new inhibitors, suggesting therapeutic potential.

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Cite This Study

Ohene-Adu et al. (2026) studied this question.

synapsesocial.com/papers/69a76755badf0bb9e87e07f7 https://doi.org/10.1016/j.aichem.2026.100111

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