Multi-Objective Structure-Based Drug Design Using Causal Discovery

Structure-based drug design (SBDD) is a critical subtask in the drug discovery process, with deep generative models playing a pivotal role. Inherently, drug design is a multi-objective task given the fact that a promising drug candidate must satisfy multiple properties. However, existing SBDD methods either focus solely on the binding affinity between molecules and target proteins while neglecting other crucial properties, or they assume that objective properties are independent of each other. Yet there are often potential relationships among properties, which can be conflicting-improving one property may lead to the deterioration of another. The lack of consideration for these relationships in current methods makes it unfeasible to generate molecules that simultaneously meet multiple objectives. To address the above issues, a multi-objective SBDD algorithm is proposed based on the diffusion model to optimize binding affinity and other drug properties simultaneously. Multiple expert networks are trained in parallel to predict properties for molecules in intermediate states and transmit gradients, and a causal graph is constructed through the causal discovery algorithm to unveil the underlying relationships among target properties. During the entire generation process, the joint distribution of target properties is decomposed in a reasonable manner according to the casual graph, and then the gradients of each property are applied to guide the optimizing direction of generation. Experimental results indicate that our model effectively optimizes multiple objectives simultaneously, generating molecules with greater drug potential compared to baseline models.