Enabling the Accelerated Discovery of Novel Chemical Probes by Integration of Crystallographic, Computational, and Synthetic Chemistry Approaches

通过整合晶体学、计算和合成化学方法，加速新型化学探针的发现

基本信息

批准号：
10613499
负责人：
Alexander Tropsha
金额：
$ 54.9万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-05-01 至 2025-04-30
项目状态：
未结题

项目摘要

ABSTRACT Identification of high-quality chemical probes, molecules with high specificity and selectivity against macromolecules, is of critical interest to drug discovery. Although millions of compounds have been screened against thousands of protein targets, small-molecule probes are currently available for only 4% of the human proteome. Thus, more efficient approaches are required to accelerate the development of novel, target-specific probes. In 2019, a new bold initiative called “Target 2035” was launched with the goal of “creating […] chemical probes, and/or functional antibodies for the entire proteome” by 2035. In support of this ambitious initiative, we propose to develop and test a novel integrative AI-driven methodology for rapid chemical probe discovery against any target protein. Here, we will build an integrative workflow where the unique XChem database of experimental crystallographic information describing the pose and nature of chemical fragments binding to the target protein will be used in several innovative computational approaches to predict the structure of organic molecules with high affinity towards specific targets. The candidate molecules will be experimentally validated and then optimized, using computational algorithms, into lead molecules to seed chemical probe development. The proposed project is structured around three following interrelated keystones: (i) Develop a novel method for ligand-binding hot-spot identification and discovery of novel chemical probe candidates; (ii) Develop novel fragment-based integrative computational approach for accelerated de novo design of chemical probes; (iii) Consensus prediction of target-specific ligands, synthesis, and experimental validation of computational hits. More specifically, we will develop a hybrid method to predict structures of high-affinity ligands for proteins for which XChem fragment screens have been completed. These approaches will be used for screening of ultra- large (>10 billion) chemical libraries to identify putative high affinity ligands within crystallographically determined pockets. Then, we will develop and employ an approach using graph convolutional neural networks for de novo design of a library of strong binders that will be evaluated to select the best candidates for chemical optimization. Finally, we will combine traditional structure-based and novel approaches, developed in this project to select consensus hit compounds against three target proteins: transcription factor brachyury, hydrolase NUDT5, and bromodomain BAZ2B. Iterative design guided by the computational algorithms, synthesis, and testing will progressively optimize molecules to micromolar leads to chemical probes for the target proteins. Completion of the proposed aims will deliver a robust integrative workflow to identify leads for chemical probes against diverse target proteins. We expect that our AI-based computational approach to convert crystallographically-determined chemical fragments into lead compounds coupled with the experimental validation of computational algorithms will accelerate the discovery of new chemical probes, expand the druggable proteome, and support future drug discovery studies

抽象的鉴定高质量的化学问题，具有高特异性和选择性的分子大分子是药物发现的关键意义。尽管已经筛选了数百万种化合物针对数千种蛋白质靶标，目前只有4％的人类出现小分子问题蛋白质组。这是需要更有效的方法来加速新颖的，特定目标的发展问题。 2019年，启动了一项名为“ Target 2035”的新大胆倡议，目的是“创建[…]化学物质到2035年，问题和/或功能性抗体”到2035年。为了支持这一雄心勃勃的倡议，我们开发和测试一种新型的集成AI驱动方法，以快速化学探针发现反对任何靶蛋白。在这里，我们将构建一个集成的工作流程，其中独特的实验Xchem数据库描述与靶蛋白结合的化学片段的姿势和性质的晶体学信息将用于几种创新的计算方法，以预测有机分子的结构对特定目标的高亲和力。候选分子将经过实验验证，然后使用计算算法优化成铅分子以播种化学探针的发展。这拟议的项目是在以下相互关联的钥匙石的三个左右结构的：（i）开发一种新颖的方法配体结合的热点鉴定和新型化学探针候选物的发现；（ii）发展小说基于碎片的集成计算方法，用于加速化学问题的新设计；（iii）目标特异性配体，合成和实验验证的共识预测。更具体地说，我们将开发一种混合方法来预测蛋白质高亲和力配体的结构哪些Xchem片段屏幕已完成。这些方法将用于筛选超级大型（> 100亿）化学库，以识别晶体学确定的假定高亲和力配体口袋。然后，我们将使用图形卷积神经网络开发和采用一种方法将评估的强粘合剂库的设计，以选择用于化学优化的最佳候选物。最后，我们将结合基于结构的传统方法和新颖的方法，在该项目中开发以选择共识击中三种靶蛋白的化合物：转录因子Brachyury，水解酶NUDT5和 Bromodomain BAZ2B。以计算算法，合成和测试为指导的迭代设计将逐渐优化分子到微摩尔会导致靶蛋白的化学问题。拟议的目标的完成将提供强大的集成工作流程，以识别化学的潜在客户针对潜水员靶蛋白的问题。我们希望我们基于AI的计算方法转换晶体学确定的化学片段成铅化合物与实验计算算法的验证将加速发现新的化学问题，扩展可药物蛋白质组，并支持未来的药物发现研究

项目成果

期刊论文数量（11）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Utilizing Low-Dimensional Molecular Embeddings for Rapid Chemical Similarity Search.

利用低维分子嵌入进行快速化学相似性搜索。

DOI：
10.1007/978-3-031-56060-6_3
发表时间：
2024
期刊：
Advances in information retrieval : ... European Conference on IR Research, ECIR ... proceedings. European Conference on IR Research
影响因子：
0
作者：
Kirchoff,KathrynE;Wellnitz,James;Hochuli,JoshuaE;Maxfield,Travis;Popov,KonstantinI;Gomez,Shawn;Tropsha,Alexander
通讯作者：
Tropsha,Alexander

Conserved coronavirus proteins as targets of broad-spectrum antivirals.

DOI：
10.1016/j.antiviral.2022.105360
发表时间：
2022-08
期刊：
Antiviral research
影响因子：
7.6
作者：
通讯作者：

Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds.

谎言和责任：高通量筛选命中的计算评估以识别人工化合物。

DOI：
10.1021/acs.jmedchem.3c00482
发表时间：
2023
期刊：
Journal of medicinal chemistry
影响因子：
7.3
作者：
Alves,ViniciusM;Yasgar,Adam;Wellnitz,James;Rai,Ganesha;Rath,Marielle;Braga,RodolphoC;Capuzzi,StephenJ;Simeonov,Anton;Muratov,EugeneN;Zakharov,AlexeyV;Tropsha,Alexander
通讯作者：
Tropsha,Alexander

An Improved Metric and Benchmark for Assessing the Performance of Virtual Screening Models.

用于评估虚拟筛选模型性能的改进指标和基准。