Abstract:

Molecular generation has emerged as a cost-effective and rapid approach for advancing the design and optimization of solvents for separation, reaction, catalysts, functional materials, pharmaceuticals, and other molecules. Existing molecular generation models, mainly based on deep learning frameworks, suffer from limited transparency and struggle to explore local chemical spaces effectively. In this work, we propose a function-driven molecular intelligent generation framework based on molecular fragment chemical space. Using molecular functional indicators as the direction for generation, and the “scaffolds-decorations” set of generated molecules as the basis, this framework explored the molecular fragment chemical space to facilitate targeted molecule generation. In addition, by using the chemical space deconstruction model proposed in this work, new structures from neighboring chemical spaces of excellent molecular structures are derived, thus enriching the variety of new molecules. By demonstrating the generation of drug-like molecules as an example, this framework starts from a smaller set of excellent molecules (644) and ultimately generates five times more excellent molecules (3158) of the same level, which illustrates the framework's ability to efficiently evolve a multitude of novel and high-quality molecules on the basis of diverse samples. This framework can combine functional objectives and constraints in actual chemical processes to promote new optimal designs such as green solvents at the process scale.

Key words: molecular generation, chemical space, molecular fragments, deep learning, intelligent chemical engineering, materials discovery

摘要：

分子定向生成能以超低人力、财力、时间成本高速推动新物质的发现和设计，因此被广泛应用于分离溶剂、反应溶剂、催化剂、功能材料、药物等分子的设计与优化。提出了一种基于分子碎片化学空间的功能驱动分子智能生成框架，以分子的功能指标为生成方向，以分子“骨架-装饰物”集合为基础，以结构碎片的化学空间为搜索范围，推动分子定向生成，深度挖掘具有潜力的新分子结构。通过生成类药性分子的案例演示，该框架能从较小的优异分子集（644）出发，最终生成五倍数量的同等级别优异分子（3158），表明该生成框架能够高效地进化出大量全新且优异的分子。该框架可结合实际化工过程中的功能目标和约束，推动过程尺度的绿色溶剂等全新最优化设计。

关键词: 分子生成, 化学空间, 分子碎片, 深度学习, 智能化工, 物质发现