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

摘要 鉴定高质量的化学探针，分子具有高度的特异性和选择性， 大分子，对药物发现至关重要。尽管已经筛选了数百万种化合物 针对成千上万的蛋白质靶点，目前只有4%的人可以使用小分子探针。 蛋白质组因此，需要更有效的方法来加速开发新的、靶特异性的药物组合物。 probes. 2019年，一项名为“目标2035”的新的大胆倡议启动，目标是“创造[...]化学品”。 到2035年，我们将研制出用于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倡议，我们 建议开发和测试一种新的综合人工智能驱动的方法，用于快速发现化学探针， 任何目标蛋白质。在这里，我们将建立一个集成的工作流程，其中独特的XChem实验数据库 描述与靶蛋白结合的化学片段的姿态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。候选分子将通过实验验证， 优化，使用计算算法，到铅分子种子化学探针的发展。的 拟议的项目是围绕以下三个相互关联的重点：（一）开发一种新的方法， 配体结合热点鉴定和发现新的化学探针候选物;（ii）开发新的 用于化学探针的加速从头设计的基于片段的综合计算方法;（iii） 目标特异性配体的一致性预测、合成和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， 哪些XChem片段筛选已完成。这些方法将用于筛选超- 大型（> 100亿）化学库，以鉴定晶体学确定的 口袋然后，我们将开发并采用一种使用图卷积神经网络的方法来从头开始 设计一个强粘合剂库，对该库进行评估以选择最佳候选物进行化学优化。 最后，我们将结合联合收割机传统的基于结构的方法和本项目开发的新方法来选择 针对三种靶蛋白的共有命中化合物：转录因子brachyury、水解酶NUDT 5和 溴结构域BAZ 2B。由计算算法、综合和测试指导的迭代设计将 逐步优化分子到微摩尔导致目标蛋白质的化学探针。 拟议目标的完成将提供一个强大的综合工作流程，以确定化学品 针对不同靶蛋白的探针。我们希望我们基于人工智能的计算方法能够将 晶体学确定的化学碎片转化为铅化合物， 计算算法的验证将加速新化学探针的发现，扩大 药物蛋白质组，并支持未来的药物发现研究

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