Efficient synthon-based modular screening of Giga-to-Terra-scale virtual libraries

基于合成子的高效模块化筛选千兆级到太级虚拟文库

• 批准号: 10710170
• 负责人: VSEVOLOD KATRITCH
• 金额: $ 41.25万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-26 至 2026-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10710170
• 关键词: Adoption; Affinity; Algorithms; Benchmarking; Binding; CNR1 gene; CNR2 gene; Chemicals; Cloud Computing; Code; Collaborations; Communities; Cyclic AMP-Dependent Protein Kinases; Development; Docking; Drug Discovery Groups; Ensure; G-Protein-Coupled Receptors; Geometry; Goals; Growth; Institution; Lead; Letters; Libraries; Ligands; Linux; Lipid Binding; Machine Learning; Nature; Nucleotides; Orphan; Phosphotransferases; Positioning Attribute; Property; Proteins; Pythons; ROCK1 gene; Reaction; Research; Resolution; Structure; Technology; Testing; Validation; Work; Writing; analog; candidate identification; cannabinoid receptor; cannabinoid receptor antagonist; chemical synthesis; clinically relevant; combinatorial; computational platform; computing resources; cost; cost effective; drug candidate; drug discovery; drug quality; drug testing; drug-like compound; improved; in silico; new technology; novel; novel strategies; open source; portability; prospective; rapid detection; rapid growth; receptor; scaffold; scale up; screening; therapeutic target; virtual; virtual library; virtual screening

ABSTRACT The goal of our proposal is to develop a scalable platform for structure-based virtual screening of Giga- and Tera- scale drug-like compound libraries, enabling streamlined discovery of high-quality drug candidates. Availability of protein target structures and Giga-scale REAL Space libraries of virtual compounds (>10 billion) position docking-based virtual screening as a key paradigm for drug discovery. However, the computational cost of Giga- scale screening becomes a major bottleneck limiting further growth of the screening libraries. Recently, we have introduced a highly scalable synthon-based technology, V-SYNTHES, which performs hierarchical structure- based screening of REadily AvaiLable for synthesis (REAL) libraries (Sadybekov et al, Nature accepted). By iteratively screening synthon-scaffold combinations, the V-SYNTHES approach makes possible rapid detection of the best-scoring compounds in the Giga-scale chemical space while performing docking of only a small fraction (~2 million) of the library. First tests of V-SYNTHES demonstrated strong enrichment in computational benchmarks and significantly improved experimental hit rates on cannabinoid receptor CB2 and ROCK1 kinase targets, while requiring 100 times less computational resources than standard virtual screenings. Building upon these preliminary results, our proposal aims to: (1) Further develop a fully automated V- SYNTHES algorithm, optimize its parameters and expand it to Tera-scale REAL libraries. (2) Apply and experimentally validate the V-SYNTHES approach on a set of therapeutic targets of different classes, which includes such challenging targets as nucleotide and lipid binding pockets, allosteric pockets, and orphan receptors (3) Establish portability of the algorithm to an open-source docking platform to further facilitate V- SYNTHES adoption in academic labs. The open-source algorithm will be distributed as a workflow for Linux clusters and computing clouds. Successful completion of this project will establish V-SYNTHES as a robust computational platform for structure-based ligand discovery in most classes of therapeutic targets, scaleable for rapidly growing REAL modular libraries. Most importantly, it will help to make fast virtual screening of the Giga-to-Tera-scale libraries broadly accessible for the whole research community with reasonable computational resources.

摘要 我们建议的目标是开发一个可扩展的平台，用于基于结构的Giga和Tera的虚拟筛选。 规模化的类药物化合物库，使高质量的候选药物的流线型发现。可用性 蛋白质靶结构和虚拟化合物（> 100亿）位置的千兆级真实的空间库 基于对接的虚拟筛选作为药物发现的关键范例。然而，Giga的计算成本- 大规模筛选成为限制筛选文库进一步增长的主要瓶颈。最近我们 引入了一种高度可扩展的基于以太网的技术V-SYNTHES，它执行分层结构- 基于真实的文库的筛选（Sadybekov等人，Nature accepted）。 通过反复筛选支架-支架组合，V-SYNTHES方法使快速 在千兆级化学空间中检测得分最高的化合物，同时仅对一种化合物进行对接。 图书馆的一小部分（约200万）。V-SYNTHES的首次测试表明， 计算基准和显着提高大麻素受体CB 2的实验命中率， ROCK 1激酶靶点，同时需要比标准虚拟筛选少100倍的计算资源。 在这些初步结果的基础上，我们的建议旨在：（1）进一步开发一个完全自动化的V- SYNTHES算法，优化其参数，并将其扩展到万亿级的真实的库。(2)应用和 实验验证了V-SYNTHES方法在一组不同类别的治疗靶点上的有效性， 包括诸如核苷酸和脂质结合口袋、变构口袋和孤儿口袋等具有挑战性的靶点。 受体（3）建立算法到开源对接平台的可移植性，以进一步促进V- SYNTHES在学术实验室的采用。开源算法将作为Linux的工作流分发 集群和计算云。该项目的成功完成将使V-SYNTHES成为一个强大的 用于在大多数类别的治疗靶点中发现基于结构的配体的计算平台， 快速增长的真实的模块化库。最重要的是，它将有助于快速虚拟筛选 整个研究社区可以广泛访问千兆到万亿级的图书馆， 资源