Abstract

Alzheimer’s disease (AD) is a severe neurodegenerative disorder whose pathological progression is closely associated with the reduced binding affinity of apolipoprotein E ε4 (ApoE4) for amyloid-β (Aβ), which impairs Aβ clearance. Existing computational molecular design approaches are largely limited to single-target optimization and therefore lack the capacity for synergistic dual-target modulation. Herein, we proposed EvoPlay-MuZero, a hybrid computational framework incorporating a dual-peptide bridging (DPB) strategy. The framework adopted latent-state planning in MuZero reinforcement learning to enhance exploration and sequence-generation efficiency in high-dimensional sequence spaces. For the first time, it enabled the automated design of bispecific peptides targeting ApoE4 and Aβ, thereby forming a synergistic molecular bridge via a flexible linker. A full-process pipeline for structural and energetic evaluation was established by integrating AlphaFold3 and PDBePISA. Benchmark experiments on the 1SSC, 2CNZ, and 3R7G datasets demonstrated that EvoPlay-MuZero substantially outperformed the vanilla EvoPlay in convergence speed and the yield of valid generated sequences. Specifically, the optimal DPB molecule (15 × 25-3A) achieved a calculated interfacial solvation energy score (ΔiG) of −29.5 kcal/mol, demonstrating substantially enhanced interface-stabilization properties compared to the native baseline control. This study provides a novel molecular intervention strategy for adjuvant therapy in AD and highlights the considerable potential of reinforcement learning in multi-target drug design.

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