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The study aims to enhance the discovery of novel PI3K inhibitors using a comprehensive in silico workflow based on copanlisib.

Optimizing PI3K Inhibition: An Integrated In Silico Approach for the Discovery of Novel PI3K Inhibitors

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The study aims to enhance the discovery of novel PI3K inhibitors using a comprehensive in silico workflow based on copanlisib.
Developed a 3D-QSAR pharmacophore model with copanlisib and tested its reliability through validation techniques.
Benchmarked five molecular docking programs, identifying CDOCKER as the most effective for pose reproduction.
Created a virtual library of copanlisib derivatives utilizing a reaction-based scaffold growth approach.
Assessed drug-likeness and utilized predictive models to screen for potential inhibitors.
Conducted molecular dynamics simulations to analyze binding mechanisms of top candidates.
Identified several compounds with high prediction scores as potential PI3K inhibitors.
The highest ranked compound was selected for further analysis through molecular dynamics simulations.
The study demonstrated improvements in the classification of inhibitors by combining with the Ludi3 scoring function.

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In silico methods identify novel PI3K inhibitors, suggesting improvements for cancer treatment.

Li et al. (2026) studied this question.

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