Physics Department, Graduate School of Pure and Applied Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8571, Japan.
Doctoral Program in Medical Sciences, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8575, Japan.
J Chem Inf Model. 2024 Jun 10;64(11):4475-4484. doi: 10.1021/acs.jcim.3c02036. Epub 2024 May 20.
Time efficiency and cost savings are major challenges in drug discovery and development. In this process, the hit-to-lead stage is expected to improve efficiency because it primarily exploits the trial-and-error approach of medicinal chemists. This study proposes a site identification and next choice (SINCHO) protocol to improve the hit-to-lead efficiency. This protocol selects an anchor atom and growth site pair, which is desirable for a hit-to-lead strategy starting from a 3D complex structure. We developed and fine-tuned the protocol using a training data set and assessed it using a test data set of the preceding hit-to-lead strategy. The protocol was tested for experimentally determined structures and molecular dynamics (MD) ensembles. The protocol had a high prediction accuracy for applying MD ensembles, owing to the consideration of protein flexibility. The SINCHO protocol enables medicinal chemists to visualize and modify functional groups in a hit-to-lead manner.
时间效率和成本节约是药物发现和开发的主要挑战。在这个过程中,期望先导化合物优化阶段能够提高效率,因为它主要利用了药物化学家的反复试验方法。本研究提出了一种基于靶位识别和下一步选择(SINCHO)的协议来提高先导化合物优化的效率。该协议选择了一个锚定原子和生长点对,这对于从 3D 复合物结构开始的先导化合物优化策略是理想的。我们使用训练数据集开发和微调了该协议,并使用前导化合物优化策略的测试数据集对其进行了评估。该协议已针对实验确定的结构和分子动力学 (MD) 集合进行了测试。由于考虑了蛋白质的灵活性,该协议对 MD 集合的应用具有很高的预测准确性。SINCHO 协议使药物化学家能够以先导化合物优化的方式可视化和修饰功能基团。