DualPG-DTA: A Large Language Model-Powered Graph Neural Network Framework for Enhanced Drug-Target Affinity Prediction and Discovery of Novel CDK9 Inhibitors Exhibiting In Vivo Anti-Leukemia Activity.

Accurate prediction of drug-target interactions constitutes a crucial foundation for drug discovery. DualPG-DTA is presented, a general framework for binding affinity prediction that integrates two pre-trained language models to generate atomic-level molecular representations and residue-level protein embeddings. The architecture constructs dual molecular-protein graphs processed through dedicated graph neural networks equipped with dynamic attention mechanisms to extract context-aware sequence-level features, which are fused via a multimodal module for affinity predictions. Benchmark results show that DualPG-DTA consistently outperforms existing models across all metrics. Applied to CDK9 inhibitor discovery, the framework is used to develop robust regression/classification models and identified compound C1 as a novel CDK9 inhibitor with an IC of 1.2 nM. C1 demonstrates exceptional CDK family selectivity alongside optimal pharmacokinetic properties, including prolonged half-life, adequate clearance, robust plasma exposure, and oral bioavailability. Notably, oral C1 demonstrated potent antitumor efficacy in a Venetoclax-resistant MV4-11 acute myeloid leukemia (AML) xenograft model, with concurrent demonstration of favorable tolerability and safety profiles. Collectively, the study not only establishes a unified framework for precise binding affinity prediction but also identifies C1 as a highly promising therapeutic lead targeting CDK9 to conquer Venetoclax resistance in AML.