MolecularCanvas：基于结构引导约束的大语言模型辅助小分子药物发现
MolecularCanvas: LLM-assisted Small-Molecule Drug Discovery via Structure-Guided Constraints
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中文总结 AI 辅助
该研究针对现有GenAI分子设计工具与专家工作流程适配差的问题，推出交互式系统MolecularCanvas，整合多类约束与工具，经用户验证可有效辅助小分子药物的迭代优化。
中文摘要 AI 辅助
小分子药物发现依赖迭代式分子优化，化学家会反复修饰候选化合物，以平衡药效、毒性、溶解度等多个相互冲突的性质。生成式人工智能（GenAI）的最新进展有望通过自动提出新分子结构或靶向修饰来加速这一过程，但现有基于GenAI的分子设计工具与专家的实际工作流程适配性较差：它们对指定分子的结构级修饰意图支持有限，对模型生成的修饰透明度不足，且缺乏与外部计算工具集成的下游性质评估支持。为解决这些挑战，我们推出MolecularCanvas，这是一个交互式系统，支持用户通过整合高级目标、结构级标注、性质约束和基于参考的偏好，迭代构建优化上下文，该上下文可指导生成多样化分子结构的候选分子。MolecularCanvas还通过为AI生成的建议提供证据来提升透明度，并通过将常用的性质评估计算工具集成到统一界面中来简化分子评估。最后，一项由12名参与者开展的用户研究表明，MolecularCanvas在帮助用户优化候选分子方面具有实用性和有效性。
英文摘要
Small-molecule drug discovery relies on iterative molecular optimization, where chemists repeatedly modify candidate compounds to balance multiple competing properties such as efficacy, toxicity, and solubility. Recent advances in generative AI (GenAI) have shown promise in accelerating this process by automatically proposing new molecular structures or targeted modifications. However, existing GenAI-based molecular design tools remain poorly aligned with experts' real-world workflows. Specifically, they offer limited support for specifying structure-level modification intents on molecules, provide insufficient transparency into model-generated modifications, and lack integrated support for downstream property evaluation with external computational tools. To address these challenges, we introduce MolecularCanvas, an interactive system that enables users to iteratively construct an optimization context by integrating high-level goals, structure-level annotations, property constraints, and reference-based preferences. This context guides the generation of candidate molecules across diverse molecular structures. MolecularCanvas further enhances transparency by providing evidence for AI-generated suggestions and streamlines molecular evaluation by integrating commonly used computational tools for property assessment into a unified interface. Finally, a user study with 12 participants demonstrates the usefulness and effectiveness of MolecularCanvas in helping users optimize candidate molecules.
