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: 10504984
• 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/10504984
• 关键词: Adoption; Affinity; Algorithms; Benchmarking; Binding; CNR1 gene; CNR2 gene; Chemicals; Cloud Computing; Code; Collaborations; Communities; Computer Analysis; 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; Pharmaceutical Preparations; Phosphotransferases; Positioning Attribute; Property; Proteins; Pythons; ROCK1 gene; Reaction; Research; Resolution; Seeds; Structure; Technology; Testing; Time; Validation; Work; analog; base; cannabinoid receptor; cannabinoid receptor antagonist; chemical synthesis; clinically relevant; cluster computing; combinatorial; computational platform; computing resources; cost; cost effective; drug candidate; drug discovery; drug quality; 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.

抽象的 我们建议的目的是为基于结构的千兆和tera-基于结构的虚拟筛选开发可扩展的平台。 比例像药物一样的化合物库，可以简化地发现高质量的候选药物。可用性 蛋白质目标结构和虚拟化合物的GIGA级真实空间库（> 100亿）位置 基于对接的虚拟筛查是药物发现的关键范例。但是，Giga的计算成本 - 比例筛选成为限制筛选文库进一步增长的主要瓶颈。最近，我们有 引入了高度可扩展的基于合成的技术V-synthes，该技术执行层次结构 - 基于可用于合成（真实）库的容易筛选（Sadybekov等人，自然接受）。 通过迭代筛选合成型组件组合，V合成方法使得可能很快 仅执行一个对接的GIGA级化学空间中最优秀的化合物的检测 图书馆的小部分（约200万）。 V合成的首次测试表现出强烈的富集 计算基准，并显着提高了大麻素受体CB2和 Rock1激酶目标，同时需要比标准虚拟筛选的计算资源少100倍。 在这些初步结果的基础上，我们的建议旨在：（1）进一步开发完全自动化的V- 合成算法，优化其参数并将其扩展到TERA级真实库。 （2）申请和 实验验证了不同类别的一组治疗靶标的V合成方法，该方法 包括诸如核苷酸和脂质结合口袋，变构袋和孤儿等具有挑战性的目标 受体（3）在开源对接平台上建立算法的可移植性，以进一步促进V- 在学术实验室中采用合成。开源算法将作为Linux的工作流程分布 集群和计算云。该项目的成功完成将建立V合成作为强大的 大多数类别的治疗靶标的基于结构的配体发现的计算平台，可缩放 快速增长的真实模块化库。最重要的是，这将有助于快速的虚拟筛选 与合理的计算 资源。