Research Department of Structural & Molecular Biology, Division of Biosciences, University College London, London, UK.
Evotec (UK) Ltd., Abingdon, Oxfordshire, UK.
Methods Mol Biol. 2024;2716:293-306. doi: 10.1007/978-1-0716-3449-3_13.
Structure-based drug design (SBDD) is rapidly evolving to be a fundamental tool for faster and more cost-effective methods of lead drug discovery. SBDD aims to offer a computational replacement to traditional high-throughput screening (HTS) methods of drug discovery. This "virtual screening" technique utilizes the structural data of a target protein in conjunction with large databases of potential drug candidates and then applies a range of different computational techniques to determine which potential candidates are likely to bind with high affinity and efficacy. It is proposed that high-throughput SBDD (HT-SBDD) will significantly enrich the success rate of HTS methods, which currently fluctuates around ~1%. In this chapter, we focus on the theory and utility of high-throughput drug docking, fragment molecular orbital calculations, and molecular dynamics techniques. We also offer a comparative review of the benefits and limitations of traditional methods against more recent SBDD advances. As HT-SBDD is computationally intensive, we will also cover the important role high-performance computing (HPC) clusters play in the future of computational drug discovery.
基于结构的药物设计(SBDD)正在迅速发展,成为更快、更具成本效益的先导药物发现方法的基本工具。SBDD 旨在为传统的高通量筛选(HTS)药物发现方法提供计算替代方案。这种“虚拟筛选”技术利用靶蛋白的结构数据与大量潜在药物候选物数据库相结合,然后应用一系列不同的计算技术来确定哪些潜在候选物可能具有高亲和力和高效性。有人提出,高通量 SBDD(HT-SBDD)将显著提高目前波动在~1%左右的 HTS 方法的成功率。在本章中,我们重点介绍高通量药物对接、片段分子轨道计算和分子动力学技术的理论和应用。我们还对传统方法与最近的 SBDD 进展相比的优缺点进行了比较性评价。由于 HT-SBDD 计算量很大,我们还将介绍高性能计算(HPC)集群在计算药物发现未来中的重要作用。