Kim Hakjean, Ryu Seongok, Jung Nuri, Yang Jinsol, Seok Chaok
Department of Chemistry, Seoul National University, Seoul, 08826, Republic of Korea.
Galux Inc, Seoul, 08738, Republic of Korea.
J Cheminform. 2024 Dec 5;16(1):137. doi: 10.1186/s13321-024-00936-8.
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.
计算分子设计的两个关键组成部分实际上是生成分子并预测这些生成分子的性质。本研究聚焦于一种通过虚拟合成和对给定目标函数进行全局优化来生成分子的有效方法。利用预训练的图神经网络（GNN）目标函数来近似四种靶标受体化合物的对接能量，我们生成了高度优化的化合物，与虚拟化合物库筛选相比，计算量减少了300 - 400倍。这些优化后的化合物与已知的高效结合剂具有相似的可合成性和多样性，并且与库化学品或已知配体相比具有显著的新颖性。这种方法称为CSearch，可有效地用于生成针对给定目标函数进行优化的化学品。通过GNN函数近似对接能量，CSearch生成的分子具有与已知抑制剂相似的预测与靶标受体的结合构象，证明了其在生成类药物结合剂方面的有效性。科学贡献 我们开发了一种方法，利用基于片段的虚拟合成的全局优化算法有效地探索类药物分子的化学空间。使用该方法生成的化合物能有效优化给定的目标函数，并且像商业库化合物一样可合成。此外，它们是多样的、新颖的类药物分子，具有与已知靶标受体抑制剂相似的性质。
