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提出 acquisition function-guided generation (AFGG) 方法，通过将采集函数倾斜的分布嵌入扩散桥，在离散空间上结合预训练生成模型与数据空间代理模型进行贝叶斯优化；在分子设计基准上，基于流的变体在多数场景中超越现有最优方法。
MolDesignBench is a new benchmark for evaluating LLM-based agents in scenario-grounded molecular design, comprising 2K instances with implicit and explicit constraints, revealing that current frontier LLMs achieve low success rates, especially in reasoning about implicit constraints and infeasibility detection.
Fraglingo introduces an attachment-aware autoregressive model for molecular design that generates molecules by jointly predicting fragment identity and attachment in a continuous latent space, enhancing property control and flexibility.
This paper presents CAi Copilot, an expert-oriented agent with three linked layers that turns molecular design intent into executable, traceable workflows, achieving the strongest performance across 45 tasks.
This paper proposes JoPMol, a jointly controlled precision molecular generative model that integrates gene expression profiles, molecular structure text, and chemical properties to generate personalized drug candidates, outperforming state-of-the-art methods.
Proposes two complementary approaches to incorporate predictive uncertainty into reinforcement learning for chemical language models, improving robustness and increasing true hit rate by 0.25 in de novo molecular design.
Introduces Constrained Flow Optimization (CFO), a framework for fine-tuning generative flow models to maximize rewards while satisfying constraints in molecular design, with theoretical guarantees and experimental validation.
EPFL researchers developed Synthegy, an AI framework that uses large language models to guide chemical retrosynthesis and reaction mechanism analysis through natural language instructions, significantly improving strategic planning for chemists.
ChemAmp introduces a tool amplification paradigm that dynamically coordinates specialized chemistry tools (UniMol2, Chemformer) as composable agents to enhance performance on molecular tasks. The framework outperforms chemistry-specialized models and reduces inference token costs by 94% compared to vanilla multi-agent systems.