Authors
Zhang, P., Han, R., Kong, X., Chen, T., Ma, J.
Abstract
Structure-based generative models often optimize single target affinity while ignoring specificity, producing candidates prone to off-target binding. We introduce SpecLig, a unified, structure-based framework that jointly generates small molecules and peptides with improved target affinity and specificity. SpecLig represents a complex as a block-based graph, combining a hierarchical SE(3)-equivariant variational autoencoder with an energy-guided geometric latent diffusion model. Chemical priors derived from block-block contact statistics are explicitly incorporated, biasing generation toward pocket-complementary fragment combinations. We benchmark SpecLig on peptide and small-molecule tasks using standard public datasets and propose precision/breadth testing paradigms to quantify specificity. Across multiple evaluations, ligand candidates generated by SpecLig usually bind the target pocket with high specificity and affinity while maintaining competitive advantages in other attributes. Ablations indicate that both hierarchical representation and energy guidance contribute to the success. Finally, we provide multiple real applications to demonstrate how SpecLig improves ligands in natural complexes to avoid potential off-target risks. SpecLig therefore provides a practical route to prioritize higher-specificity designs for downstream experimental validation. The codes are available at: https://github.com/CQ-zhang-2016/SpecLig.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 09 Nov 2025.
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