Oracle-limited molecular optimization gives reward only after a complete molecule is generated, while each rollout requires many local next-token decisions. This delayed-feedback interface makes molecular policy optimization myopic: an optimizer can learn that a molecule was good without knowing which intermediate actions made it good.
arXiv:2609.36683v1 Announce Type: new
Abstract: Molecular optimization is inherently iterative: a candidate is proposed, evaluated against several objectives, and revised while preserving a relations...
By Shicheng Fang, Yuxin Wang, Zhuo Yang, Xiaohu Xu, Jiahao Lu, Chuanyuan Tan, Tong Zhu, Yining Zheng, Xipeng Qiu
arXiv:2607. 02834v1 Announce Type: new Abstract: Molecular optimization often starts from a pretrained generative model that captures a broad prior over valid molecular structures.
By Trevor Chen, Ariel Dai, Jason Yang, Riccardo De Santi, Daniel Khalil, Wenda Chu, Nate Gruver, Pranav Murugan, Alexander F. G. Goldberg, Maruan Al-Shedivat, Yisong Yue
arXiv:2606. 18961v1 Announce Type: new Abstract: Protein language models (PLMs) have emerged as powerful tools for controllable biomolecular design, yet their post-training adaptation typically relies on costly wet-lab validation or curated preference datasets.
By Lanqing Li, Shentong Mo, Yang Yu, Pheng-Ann Heng
arXiv:2609.00189v1 Announce Type: new
Abstract: Goal-directed optimization is essential for steering molecular generators to propose candidates with desired properties. However, it is often implement...
By Shiyun Wa, Yifei Wang, Anna G. Green, Simone Sciabola, Ye Wang
arXiv:2607. 28437v1 Announce Type: new Abstract: Molecular optimization is commonly performed under a limited oracle budget, which makes deciding what to evaluate as important as deciding what to generate.
By Jiannan Yang, Veronika Thost, Xiang Ling, Tengfei Ma