Hyp-retro: hypernetwork-guided preference-conditioned retrosynthetic planner.

Retrosynthetic planning is a core task in computer-aided synthesis design. Existing multi-step retrosynthesis methods mainly focus on route accessibility and search success rates. However, in practical synthesis, route quality depends partly on yield, whose importance may vary across applications. Existing methods often struggle to adjust their search strategies according to changing yield preferences. To address this problem, we propose Hyp-Retro, a hypernetwork-guided and preference-conditioned retrosynthetic planning model. Hyp-Retro first collects candidate-pool decision data through a yield-guided search process. It then constructs a hypernetwork-driven tree policy network conditioned on yield preference, allowing the model to adjust candidate-node scores under different preference settings. On this basis, online reinforcement learning is employed to fine-tune the policy network, further enhancing the model's long-horizon decision-making capability and preference alignment in realistic search environments. Experimental results show that Hyp-Retro outperforms comparative methods in terms of search success rate and route yield. Moreover, it can adaptively adjust its planning strategy under different yield preferences, thereby generating high-quality retrosynthetic routes that better satisfy target-specific requirements.