AI-driven

Background: Traditional drug discovery methods, such as high-throughput screening (HTS), are often inefficient and costly, especially in complex areas like oncology. The BRAF V600E mutation is a validated therapeutic target in cancers such as melanoma, thyroid carcinoma, and colorectal cancer. However, existing BRAF inhibitors face challenges like acquired resistance and off-target toxicity. Artificial intelligence (AI) has emerged as a transformative tool for designing novel inhibitors more efficiently. Methods: This study employed REINVENT 4, an advanced machine learning (ML) framework using recurrent neural networks and transformer architectures, for targeted generation and property optimization of BRAF V600E inhibitors, integrating reinforcement learning (RL) for drug-likeness optimization and transfer learning (TL) for mutation-specific design. Molecular docking and dynamics simulations were used to evaluate binding affinity and stability. Results: The AI-driven approach generated 41,721 novel BRAF V600E inhibitor candidates with enhanced drug-likeness (mean Quantitative Estimate of Drug-likeness (QED) score: 0.61 ± 0.17 Discussion: This work highlights a reproducible, integrated AI-driven workflow demonstration for targeted inhibitor generation. The generated inhibitors exhibit favorable drug-like properties and inhibitory activity, offering a scalable solution for designing safer cancer therapies. Experimental validation is needed to address potential discrepancies between computational predictions and biological behavior.