Antibodies are essential therapeutic molecules, and their complementarity-determining regions (CDRs) form the primary antigen-recognition interface. Recent protein generative models have demonstrated broad capabilities in biomolecular design, yet post-training strategies for downstream objectives remain limited. Standard denoising training operates on noisy states obtained by perturbing native structures, whereas recursive generation proceeds through model-generated intermediate states. For flexible antibody CDR loops such as CDR-H3, this mismatch can allow backbone deviations to accumulate along the denoising trajectory and compromise antigen-facing loop geometry. We introduce ABOPD, an antibody design framework based on on-policy distillation that leverages privileged native geometry during training to supervise states visited along the model's own denoising trajectories. With this fine-grained structural supervision, ABOPD substantially improves structural recovery on RAbD CDR-H3 generation, reducing RMSD by 0.42 {\AA} (from 2.37 {\AA} to 1.95 {\AA}) and outperforming supervised fine-tuning and offline distillation controls, offering a path to higher-fidelity protein design.
Zhuo Yang, Jiaying He, Jiaqing Xie et al.· 0 citations
Many real‐world scientific and industrial applications require the optimization of expensive black‐box functions. Bayesian optimization (BO) provides an effective framework but often struggles with local optima and lacks interpretability. This paper introduces reasoning BO, a novel framework leveraging reasoning models to guide BO sampling while incorporating multi‐agent systems and knowledge graphs for online knowledge accumulation. We evaluate our approach across 10 diverse tasks, including synthetic functions and complex real‐world chemical optimizations. Reasoning BO progressively refines sampling strategies through real‐time insights and hypothesis evolution, identifying high‐performing regions effectively. In the direct arylation task, our method significantly outperformed traditional BO, increasing yield from 25.20% to 60.07%. Furthermore, we demonstrate that smaller LLMs, after post‐training, can achieve performance comparable to larger counterparts. This framework establishes an intelligent cost‐effective optimization system for scientific discovery, combining LLM reasoning with structured knowledge management.
Zhuo Yang, Daolang Wang, Lingli Ge et al.· Materials Genome Engineering...· 0 citations
AgentFold is presented, a multi-agent framework that formulates folding-model development as a closed-loop search over executable code variants and improves the best lDDT by 7.5% over independent Codex proposals and outperforms a random-search control.
Mingquan Liu, Jiangyue Chen, Hanqun Cao et al.· 0 citations
Applications in materials analysis, molecule design, and protein or antibody screening, together with experiments on scientific reading, idea generation, molecule generation, and antibody screening, show that SCION outperforms existing autonomous research-agent baselines, especially in decomposition, verification, refinement, and memory reuse.
Y. Zheng, Yuxin Wang, Jiahao Lu et al.· 0 citations