Aug 2026· Molecular diversity· 0 citations· 27 references
Medicine
TL;DR
Experimental results show that Hyp-Retro outperforms comparative methods in terms of search success rate and route yield, and it can adaptively adjust its planning strategy under different yield preferences, thereby generating high-quality retrosynthetic routes that better satisfy target-specific requirements.
Results indicate that incorporating molecular cost information into heuristic search can improve the practicality and economic efficiency of retrosynthetic planning.
Shuan Liu, Jing-Wen Wang, Shao-Ye Zhang et al.· European journal of medicina...· 0 citations
This work proposes GuideRetro, a synthesizability-aware framework for multi-step retrosynthetic planning that integrates global syn-thesizability knowledge into step-wise retrosyn-thetic prediction and improves planning accuracy and search efficiency under realistic retrosynthetic settings.
Yu-Jie Chen, Ajie Lin, Tengfei Ma et al.· Proceedings of the Thirty-Fi...· 0 citations
This work introduces RobustSGPO, which specifies the requested edit, constructs and checks the patch, and continues search from either the incumbent or retained snapshots, and reduces source-task degradation after a shift, whereas random retention reaches a higher destination endpoint.
Zi-Bo Zhao, Ji-Jun Shi, Mo-Qing Zhou et al.· 1 citation
PGMPO is proposed, a novel learning framework consisting of a simple but effective multi-policy modeling approach that allows a single network to represent multiple decision-makers, and a preference-driven model optimization method that effectively guides policies to learn diverse and specialized problem-solving strate...
Inguk Choi, Woo-Jin Shin, Sang-Hyun Cho et al.· Proceedings of the Thirty-Fi...· 0 citations
POLO (Preference-guided Optimization for Lead Optimization), a multi-turn Reinforcement Learning framework for sample-efficient lead optimization, which extracts learning signals from each trajectory at two complementary levels: trajectory-level policy optimization reinforces successful multi-turn strategies, while tur...
Ziqing Wang, Yibo Wen, William Pattie et al.· Proceedings of the 32nd ACM...· 0 citations
This work proposes \methodname, a stability-guided active-set controller for controlled objective admission, a stability-guided active-set controller for controlled objective admission in reward-vector RLHF, which positions objective-entry timing as a concrete control variable in reward-vector RLHF.
Yong-Qi Tong, Z. Zhang, Ruirui Wang et al.· 1 citation
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