Tang Qiaosi, Ding Daoyun, Yuan Xiaoyong, Seabra Gustavo, Ramdhan Peter A, Liu Chi-Yuan, Thai My T, Li Chenglong, Luesch Hendrik, Li Yanjun
Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, USA.
School of Computer, Data & Information Sciences, University of Wisconsin-Madison, Madison, WI 53706, USA.
bioRxiv. 2025 Jul 17:2025.07.11.664424. doi: 10.1101/2025.07.11.664424.
Designing novel molecules with desired bioactivity remains a fundamental challenge in drug discovery. Most molecular design methods follow target-based drug discovery paradigms that rely on well-defined drug targets, thereby limiting their applicability to diseases lacking known targets or reference compounds. Here we introduce Morphology-Guided Molecule Generation (MGMG), a phenotypic drug discovery-oriented approach that integrates cellular morphological profiles from compound treatments with molecular textual descriptions without requiring molecular target information. Cell morphology offers the guidance on desired bioactivity-relevant phenotypic effects, while textual descriptions provide direct and interpretable cues about molecular structure. Leveraging complementary structural and bioactivity context, MGMG significantly enhances molecule generation performance, especially in scenarios where textual descriptions are under-informative or morphological signals are weak. MGMG can also be applied to genetic perturbations, enabling activator design from gene overexpression morphology without requiring knowledge of reference compound structure. In addition, in silico docking demonstrates that MGMG-generated molecules, despite lacking target information, exhibit binding affinities comparable to reference compounds, preserving key interactions while introducing structural diversity. Overall, MGMG jointly utilizes morphological and textual description inputs to guide molecule generation, enabling diverse, bioactivity-aware compound design in a target-agnostic fashion.
设计具有所需生物活性的新型分子仍然是药物发现中的一项基本挑战。大多数分子设计方法遵循基于靶点的药物发现范式，这些范式依赖于明确的药物靶点，从而限制了它们对缺乏已知靶点或参考化合物的疾病的适用性。在这里，我们介绍形态学引导分子生成（MGMG），这是一种面向表型药物发现的方法，它将化合物处理后的细胞形态学特征与分子文本描述相结合，而无需分子靶点信息。细胞形态学为与所需生物活性相关的表型效应提供指导，而文本描述则提供有关分子结构的直接且可解释的线索。利用互补的结构和生物活性背景，MGMG显著提高了分子生成性能，尤其是在文本描述信息不足或形态学信号较弱的情况下。MGMG还可应用于基因扰动，无需参考化合物结构知识即可从基因过表达形态设计激活剂。此外，计算机对接表明，MGMG生成的分子尽管缺乏靶点信息，但表现出与参考化合物相当的结合亲和力，在引入结构多样性的同时保留了关键相互作用。总体而言，MGMG联合利用形态学和文本描述输入来指导分子生成，以一种无需靶点信息的方式实现了多样化的、具有生物活性意识的化合物设计。
