MolSculptor: An Adaptive Diffusion-Evolution Framework Enabling Generative Drug Design for Multitarget Affinity and Selectivity

Clinical Snapshot

Key Findings

• P Value: Not reported in abstract

• Effect Size: Significant outperformance of state-of-the-art methods (specific metrics not provided in abstract)

• Primary Outcome: Successful generation of multitarget inhibitors with superior predicted affinity profiles compared to experimental references

• Nnt Or Sensitivity: Successful experimental validation of novel dual-target CRPC inhibitor

• Confidence Interval: Not reported in abstract

Clinical Application

High feasibility for computational drug design teams with access to structural biology data and synthesis capabilities Highly relevant to Australian pharmaceutical research initiatives and TGA drug development pathways, particularly for precision oncology approaches supported by the Medical Research Future Fund Researchers and pharmaceutical companies developing multitarget therapeutics for complex diseases including cancer

Abstract

The rational design of molecules with tailored activity/selectivity across multiple protein targets is crucial for developing therapies for complex diseases like cancer, yet it remains a formidable challenge. While deep generative models show immense promise, their application to these tasks faces fundamental challenges, as they struggle to incorporate the structural information on multiple distinct protein pockets and require vast multitarget data sets or specialized expert knowledge that are rarely available. Here we introduce MolSculptor, an adaptive diffusion-evolution framework designed to generate inhibitors for any combination of on- and off-targets, circumventing the need for target-specific training data or prior expert knowledge. MolSculptor unifies both de novo design and lead optimization, provides a versatile workflow applicable to different stages of drug discovery, and allows for direct conditioning on key drug-like properties. It integrates a 3D-aware surrogate model to enable flexible guidance for any set of specified on- and off-targets. Furthermore, MolSculptor employs an active learning protocol to adaptively refine this guidance, ensuring high performance even in data-scarce scenarios. We demonstrate MolSculptor on a series of challenging multitarget and selective inhibitor design tasks, where it significantly outperforms state-of-the-art methods in generating high-quality candidates that satisfy all complex constraints. Notably, many of the generated molecules exhibit predicted affinity profiles superior to those of experimentally validated references. Using MolSculptor, we successfully designed and synthesized a novel and potent dual-target inhibitor for castration-resistant prostate cancer (CRPC), whose inhibitory activity was confirmed through wet-lab validation. MolSculptor provides a powerful and generalizable paradigm for designing ligands with complex, multitarget activity profiles, paving the way for data-efficient solutions to complex therapeutic problems.

References

1. 1.Li, Y., Dong, H., Lin, X., Hao, Y., Xue, Y., Zhang, J., Wu, Y., Zhou, J., & Gao, Y. Q. (2026). MolSculptor: An Adaptive Diffusion-Evolution Framework Enabling Generative Drug Design for Multitarget Affinity and Selectivity. Journal of Chemical Theory and Computation. https://doi.org/10.1021/acs.jctc.6c00395