arXiv:2607. 08404v1 Announce Type: cross Abstract: Current computational approaches for drug design typically focus on generating molecules conditioned on specific targets or general molecular properties, often neglecting the influence of disease context on target behavior and therapeutic outcomes.
By Ali Motahharynia, Mohammadreza Ghaffarzadeh-Esfahani, Mahsa Sheikholeslami, Navid Mazrouei, Matin Irajpour, Yousof Gheisari, Hajar Sirous
SurfSpec is a new framework for lead optimization that improves drug specificity without needing off‑target structures. It works by measuring and reducing the geometric mismatch between a ligand and its target pocket, using the triangle inequality to infer a lower bound on mismatch to off‑target pockets. In tests on the CrossDocked2020 dataset, SurfSpec lowers geometric mismatch and achieves higher empirical specificity while still improving target affinity.
By Minyeong Hwang, Yoorim Gang, Ziseok Lee, Wooyeol Lee, Young Bin Park, Jae-Mun Choi, Kyungsu Kim, Eunho Yang
ProbeMatchDTI is a new framework for drug‑target interaction prediction that uses probe‑driven pattern matching to preserve weak biochemical signals. It introduces IterProbe, which retains contextual states across refinement depths and selects them with learnable probes, and BindingProbe, which models drug‑protein complementarity at both local and whole‑pair levels. Experiments show that ProbeMatchDTI outperforms existing methods, improving AUC‑ROC by 2.0% on BindingDB and 0.5% on DrugBank, and its predictions can be integrated into downstream drug‑discovery workflows.
By Quan Hao, Mengyue Fan, Zifan Dong, Youru Li, Jianduo Zhao, Lechuan Xu, Hao Zhang, Fei Xia, Jigang Wang, Chong Qiu, Liguo Zhang
SurfSpec is a lead‑optimization framework that improves drug specificity without needing off‑target structures. By measuring and reducing the geometric mismatch between a ligand and its target pocket, SurfSpec provides a conservative lower bound on specificity against geometrically separated off‑target pockets. The method iteratively grows ligands toward under‑occupied target surface patches, alternating between linker generation and refinement, and demonstrates superior empirical specificity on the CrossDocked2020 test set while maintaining competitive target affinity.
arXiv:2606. 00555v1 Announce Type: new Abstract: Structure-based drug design increasingly employs LLM agents to iteratively refine ligands against a target pocket, yet a viable ligand must satisfy two often-conflicting objectives -- binding affinity and druggability -- which single optimization steps rarely improve together.
By Zaifei Yang, Weiyu Chen, Yaqing Wang, James Kwok
arXiv:2506. 14488v2 Announce Type: replace-cross Abstract: Structure-based drug design (SBDD) models are central to modern pharmaceutical research, enabling the rational exploration of protein-ligand interactions at atomic resolution.
By Dong Xu, Zhangfan Yang, Junchuang Cai, Sisi Yuan, Zexuan Zhu, Jianqiang Li, Junkai Ji