EVOSYNTH: Enabling Multi-Target Drug Discovery through Latent Evolutionary Optimization and Synthesis-Aware Prioritization

Abstract

Complex diseases, such as cancer and neurodegeneration, feature interconnected pathways, making single-target therapies ineffective due to pathway redundancy and compensatory mechanisms. Polypharmacy, which combines multiple drugs to target distinct proteins, addresses this but often leads to drug-drug interactions, cumulative toxicity, and complex pharmacokinetics. To overcome these challenges, we introduce EO_SCPLOWVOC_SCPLOWSO_SCPLOWYNTHC_SCPLOW, a modular framework for multi-target drug discovery that combines latent evolution and synthesis-aware prioritization to generate and prioritize candidates with high translational potential. Latent evolution navigates a chemically and functionally informed latent space to identify candidates with strong predicted affinity across multiple targets. Synthesis-aware prioritization evaluates both retrosynthetic feasibility and the trade-off between synthetic cost and therapeutic reward, enabling practical and efficient candidate selection. Applied to dual inhibition of JNK3 and GSK3{beta} in Alzheimers disease and PI3K and PARP1 in ovarian cancer, EO_SCPLOWVOC_SCPLOWSO_SCPLOWYNTHC_SCPLOW consistently outperforms baseline generative models, achieving higher predicted affinities, improved scaffold diversity, and lower synthesis costs. These findings highlight EO_SCPLOWVOC_SCPLOWSO_SCPLOWYNTHC_SCPLOWs ability to integrate target-driven generation with practical synthesizability, establishing a scalable framework for multi-target and polypharmacological drug discovery. Our source code and data to reproduce all experiments is publicly available on GitHub at: https://github.com/HySonLab/EvoSynth.