VeGA-RX and VeGA-SCX: Controllable SMARTS-Guided Generative Transformers for Precision-Driven <i>De Novo</i> Drug Design

= 0.6) reducing structural alerts and improving drug-likeness without additional reinforcement learning (RL). Retrospective benchmarking on five pharmacological targets using a strict leakage-safe holdout strategy reveals a clear functional dichotomy: VeGA-SCX maximizes the recovery of bioactive chemotypes (e.g., >90% on mTORC1), while VeGA-RX effectively navigates complex chemical spaces with superior creativity. Comparative analysis against LDMol, a state-of-the-art text-to-molecule diffusion model, confirms the advantages of our autoregressive approach, which delivers higher chemical validity, stricter adherence to constraints, and significantly lower computational latency. In addition, we validate the framework in data-scarce scenarios targeting DCAF1 and WRN helicase, prioritizing candidate ligands exhibiting stable computational binding modes supported by docking and molecular dynamics simulations. Overall, this framework provides a controllable and interpretable strategy for precision-oriented generative chemistry. The complete workflow is open-source and available on GitHub.