The two key components of computational molecular design are virtually generating molecules and predicting the properties of these generated molecules. This study focuses on an effective method for molecular generation through virtual synthesis and global optimization of a given objective function. Using a pre-trained graph neural network (GNN) objective function to approximate the docking energies of compounds for four target receptors, we generated highly optimized compounds with 300–400 times less computational effort compared to virtual compound library screening. These optimized compounds exhibit similar synthesizability and diversity to known binders with high potency and are notably novel compared to library chemicals or known ligands. This method, called CSearch, can be effectively utilized to generate chemicals optimized for a given objective function. With the GNN function approximating docking energies, CSearch generated molecules with predicted binding poses to the target receptors similar to known inhibitors, demonstrating its effectiveness in producing drug-like binders. Scientific Contribution We have developed a method for effectively exploring the chemical space of drug-like molecules using a global optimization algorithm with fragment-based virtual synthesis. The compounds generated using this method optimize the given objective function efficiently and are synthesizable like commercial library compounds. Furthermore, they are diverse, novel drug-like molecules with properties similar to known inhibitors for target receptors.

中文翻译：

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CSearch：通过虚拟合成和全局优化进行化学空间搜索


计算分子设计的两个关键组成部分是虚拟生成分子和预测这些生成分子的特性。本研究侧重于通过给定目标函数的虚拟合成和全局优化来产生分子的有效方法。使用预先训练的图形神经网络 （GNN） 目标函数来近似化合物对四个靶受体的对接能量，与虚拟化合物库筛选相比，我们生成了高度优化的化合物，计算工作量减少了 300-400 倍。这些优化的化合物表现出与已知高效能结合剂相似的合成性和多样性，并且与文库化学品或已知配体相比具有明显的新颖性。这种方法称为 CSearch，可以有效地用于生成针对给定目标函数优化的化学物质。通过接近对接能量的 GNN 函数，CSearch 生成了与已知抑制剂相似的具有预测与靶受体结合姿势的分子，证明了其在产生药物样结合剂方面的有效性。科学贡献 我们开发了一种使用基于片段的虚拟合成的全局优化算法有效探索药物样分子化学空间的方法。使用这种方法生成的化合物可以有效地优化给定的目标函数，并且可以像商业库化合物一样合成。此外，它们是多种新型药物样分子，具有与已知靶受体抑制剂相似的特性。