Transformer-accelerated discovery of inhibitors targeting the RpsAΔ438 deletion in PZA-resistant tuberculosis

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protein by coupling a Transformer-based deep learning framework for high-throughput virtual screening with subsequent molecular docking, Gaussian accelerated molecular dynamics simulations, MM/GBSA free energy calculations and ADMET prediction. This strategy enabled the efficient identification of potential inhibitors from expansive chemical space and allowed for the in-depth evaluation of compound interactions with the mutant protein. Among the screened candidates, Z2568748600 was identified as the most promising compound based on comprehensive computational assessments. Collectively, this approach significantly improved the discovery efficiency of promising inhibitors against pyrazinamide-resistant Mycobacterium tuberculosis.