Pretrained graph transformer-driven de novo design and machine learning prioritization of computationally predicted ENR-InhA (InhA) inhibitors with potential relevance to drug-resistant Mycobacterium tuberculosis

Multidrug-resistant tuberculosis (MDR-TB) undermines frontline therapy, and conventional drug discovery is slow and resource intensive. We present an integrated computational pipeline for the de novo design of inhibitors of a single clinically validated target, the NADH-dependent enoyl-[acyl-carrier-protein] reductase of Mycobacterium tuberculosis (ENR-InhA, encoded by inhA). Bioactive compounds were retrieved from ChEMBL and curated to 80 with equality-qualified IC50 values. Four conventional machine-learning models and one deep neural network were trained on Morgan and MACCS fingerprints to predict pIC50. DrugGEN, a pretrained graph-transformer generative adversarial network, designed de novo molecules. Of 74 generated, 67 were novel relative to the curated set (nearest-neighbour Tanimoto