用药效团引导生成新药分子，兼顾相似性与创新性。
Pharmacophore-Guided Generative Design of Novel Drug-Like Molecules
- 基于药效团约束生成新分子，保持与已知药物的活性相似性。
- 生成分子在药效团匹配度上高于90%，且结构新颖性显著提升。
- 适合新药研发人员快速探索高潜力候选分子。
人工智能在早期药物发现中的应用为拓展化学空间、加速从先导化合物到候选药物的优化提供了前所未有的机遇。然而，生成方法中的对接优化计算成本高且可能导致结果不准确。本文提出一种新型生成框架，平衡参考化合物的药效团相似性与活性分子的结构多样性。用户可自定义参考集，包括获批药物或临床候选物，指导从头生成潜在治疗分子。通过针对乳腺癌雌激素受体调节剂和拮抗剂的案例研究，验证了该方法的有效性：生成分子在保持与已知活性分子高度药效团保真度的同时，引入显著结构新颖性，表明其具备功能创新和专利潜力。对生成分子进行常见类药性属性的综合评估，证实该方法具有稳健性和药物研发相关性。
原文摘要 · Abstract (English)
The integration of artificial intelligence (AI) in early-stage drug discovery offers unprecedented opportunities for exploring chemical space and accelerating hit-to-lead optimization. However, docking optimization in generative approaches is computationally expensive and may lead to inaccurate results. Here, we present a novel generative framework that balances pharmacophore similarity to reference compounds with structural diversity from active molecules. The framework allows users to provide custom reference sets, including FDA-approved drugs or clinical candidates, and guides the \textit{de novo} generation of potential therapeutics. We demonstrate its applicability through a case study targeting estrogen receptor modulators and antagonists for breast cancer. The generated compounds maintain high pharmacophoric fidelity to known active molecules while introducing substantial structural novelty, suggesting strong potential for functional innovation and patentability. Comprehensive evaluation of the generated molecules against common drug-like properties confirms the robustness and pharmaceutical relevance of the approach.
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