CtrlMol: Controllable Molecular Generation for Drug Design via Flexible Condition-Adaptive Diffusion

Conditional molecular generation is critical for drug design, where generated molecules need to satisfy varying design requirements. However, existing molecular generation methods lack flexibility in handling diverse reference conditions and controllability in adjusting generation toward desired molecular profiles, limiting their ability to meet some customized requirements. To address these limitations, we propose CtrlMol, a novel controllable molecular generation framework via flexible condition-adaptive diffusion model. CtrlMol achieves flexibility through Arbitrary-Count Condition Encoding that accommodates zero or more retrieved molecular and pocket conditions, followed by Self-Adaptive Modulation aggregating them into the denoising process in a unified manner. CtrlMol also achieves controllability through Independent Dual-Axis Classifier-Free Guidance that separately adjusts the guidance strengths of molecular and pocket conditions during inference, allowing controllable generation trade-offs between precision and diversity. Experiments on the TarPass benchmark demonstrate that CtrlMol consistently outperforms competing methods, achieving 28.8% improvement in target specificity and superior molecular property control. Case studies on a cardiovascular target ADRB2 further validate that the flexibility and controllability jointly enable effective adjustment of the generation distribution.