ActivityDiff: a diffusion model with positive and negative activity guidance for de novo drug design
Abstract
Motivation: De novo drug design requires not only promoting desired target activity, but also avoiding undesired target interactions that compromise selectivity and safety. However, existing generative models largely focus on optimizing positive activity, while overlooking negative activity information that could help suppress off-target effects during molecular design.
Results: We present ActivityDiff, a classifier-guided diffusion framework for activity-controlled molecular generation. Unlike conventional approaches that rely primarily on positive activity optimization, ActivityDiff explicitly incorporates both positive and negative guidance through separately trained drug-target classifiers. This design enables the model not only to promote desired target activities, but also to suppress harmful off-target interactions during generation. Experiments show that ActivityDiff effectively supports various drug design tasks, including single- and dual-target generation, fragment-constrained dual-target design, selective generation for improved target specificity, and reduction of off-target effects. These results demonstrate that classifier-guided diffusion with explicit negative guidance provides an effective strategy for jointly optimizing efficacy and safety in molecular design.
Availability and implementation: The source code can be obtained from https://github.com/e-yi/ActivityDiff.
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