ActivityDiff: A diffusion model with Positive and Negative Activity Guidance for De Novo Drug Design
This addresses the problem of integrated control over efficacy and safety in drug design for pharmaceutical researchers, representing a novel paradigm rather than an incremental improvement.
The paper tackles the challenge of precise control over multiple biological activities in de novo drug design by proposing ActivityDiff, a diffusion model with positive and negative activity guidance that enhances desired activities while minimizing harmful off-target effects, demonstrating effectiveness in tasks like single-/dual-target generation and off-target reduction.
Achieving precise control over a molecule's biological activity-encompassing targeted activation/inhibition, cooperative multi-target modulation, and off-target toxicity mitigation-remains a critical challenge in de novo drug design. However, existing generative methods primarily focus on producing molecules with a single desired activity, lacking integrated mechanisms for the simultaneous management of multiple intended and unintended molecular interactions. Here, we propose ActivityDiff, a generative approach based on the classifier-guidance technique of diffusion models. It leverages separately trained drug-target classifiers for both positive and negative guidance, enabling the model to enhance desired activities while minimizing harmful off-target effects. Experimental results show that ActivityDiff effectively handles essential drug design tasks, including single-/dual-target generation, fragment-constrained dual-target design, selective generation to enhance target specificity, and reduction of off-target effects. These results demonstrate the effectiveness of classifier-guided diffusion in balancing efficacy and safety in molecular design. Overall, our work introduces a novel paradigm for achieving integrated control over molecular activity, and provides ActivityDiff as a versatile and extensible framework.