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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

基本信息

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
项目类别：
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

项目摘要

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