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探针、具有高特异性和选择性的分子尽管已经筛选了数百万种化合物，但大分子对药物发现至关重要。针对数千种蛋白质靶标，小分子探针目前仅适用于 4% 的人类因此，需要更有效的方法来加速新颖的、针对特定目标的蛋白质组的开发。 2019 年，一项名为“Target 2035”的大胆新举措启动，目标是“创造……”化学物质。到 2035 年，我们将开发出针对整个蛋白质组的探针和/或功能性抗体。为了支持这一雄心勃勃的计划，我们建议开发和测试一种新颖的综合人工智能驱动方法，用于快速发现化学探针在这里，我们将构建一个集成的工作流程，其中包含独特的 XChem 实验数据库。描述与靶蛋白结合的化学片段的构成和性质的晶体学信息将用于多种创新计算方法来预测有机分子的结构候选分子将经过实验验证，然后对特定目标具有高亲和力。使用计算算法优化为先导分子，以促进化学探针的开发。拟议的项目围绕以下三个相互关联的基石构建：（i）开发一种新方法 (ii) 开发小说 (iii) 基于片段的综合计算方法，用于加速化学探针的从头设计；目标特异性配体的共识预测、合成以及计算命中的实验验证。更具体地说，我们将开发一种混合方法来预测蛋白质的高亲和力配体的结构 XChem 片段筛选已经完成，这些方法将用于筛选超分子。大型（>100 亿）化学库，用于在晶体学确定的范围内识别假定的高亲和力配体然后，我们将开发并采用一种使用图卷积神经网络从头开始的方法。设计一个强粘合剂库，对其进行评估以选择化学优化的最佳候选者。最后，我们将结合传统的基于结构的方法和本项目中开发的新颖方法来选择共识击中了针对三种靶蛋白的化合物：转录因子 brachyury、水解酶 NUDT5 和 bromodomain BAZ2B 由计算算法、合成和测试指导的迭代设计。逐步将分子优化至微摩尔，从而产生针对目标蛋白质的化学探针。完成拟议的目标将提供强大的综合工作流程来识别化学先导化合物我们期望我们基于人工智能的计算方法能够转换不同的目标蛋白。晶体学确定的化学片段转化为先导化合物，并结合实验计算算法的验证将加速新化学探针的发现，扩大可成药的蛋白质组，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：
发表时间：
2024-03
期刊：
影响因子：
0
作者：
Kirchoff, Kathryn E;Wellnitz, James;Hochuli, Joshua E;Maxfield, Travis;Popov, Konstantin I;Gomez, Shawn;Tropsha, Alexander
通讯作者：
Tropsha, Alexander

BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening.

BigBind：从非结构数据中学习以进行基于结构的虚拟筛选。

DOI：
发表时间：
2024-04-08
期刊：
影响因子：
0
作者：
Brocidiacono, Michael;Francoeur, Paul;Aggarwal, Rishal;Popov, Konstantin I;Koes, David Ryan;Tropsha, Alexander
通讯作者：
Tropsha, Alexander

Small Molecule Antiviral Compound Collection (SMACC): a database to support the discovery of broad-spectrum antiviral drug molecules.

小分子抗病毒化合物集合 (SMACC)：支持广谱抗病毒药物分子发现的数据库。

DOI：
发表时间：
2022-07-11
期刊：
影响因子：
0
作者：
Martin, Holli;Melo;Korn, Daniel;Eastman, Richard T;Rai, Ganesha;Simeonov, Anton;Zakharov, Alexey V;Muratov, Eugene;Tropsha, Alexander
通讯作者：
Tropsha, Alexander

Small molecule antiviral compound collection (SMACC): A comprehensive, highly curated database to support the discovery of broad-spectrum antiviral drug molecules.

小分子抗病毒化合物集合 (SMACC)：一个全面、精心策划的数据库，支持广谱抗病毒药物分子的发现。

DOI：
发表时间：
2023-09
期刊：
影响因子：
7.6
作者：
Martin, Holli;Melo;Korn, Daniel;Eastman, Richard T;Rai, Ganesha;Simeonov, Anton;Zakharov, Alexey V;Muratov, Eugene;Tropsha, Alexander
通讯作者：
Tropsha, Alexander

PLANTAIN: Diffusion-inspired Pose Score Minimization for Fast and Accurate Molecular Docking.

车前草：受扩散启发的姿势评分最小化，用于快速准确的分子对接。