virtual compound screening using gene expression

项目摘要 今天的技术允许在非常低的水平上对疾病和药物的数千个基因表达特征进行分析。 成本这项名为“利用基因表达进行虚拟化合物筛选”的提案旨在开发新颖的数据 科学的方法来利用新兴的基因表达谱来发现新药。此前我们 开发了一种称为RGES的评分功能，以量化药物逆转疾病基因表达的效力 基于药物和疾病的表达谱。我们观察到RGES与药物疗效相关。 利用这个想法，我们和其他人确定了可以重新用于治疗许多疾病的药物。然而，在这方面， 该方法目前不支持新化合物筛选或先导物优化。执行这一 一种大规模筛选大型化合物文库的方法，我们首先需要生成基因表达谱 库化合物的。然而，由于缺乏新的大规模基因表达谱， 化合物，虚拟化合物筛选是不可能的，直到最近的努力，包括我们的证明， 仅基于化学结构预测基因表达的可行性。因此，该项目的目标是 开发新的机器学习方法，以提高药物基因表达预测的性能， 在实际药物发现中利用预测的谱。为了实现这些目标，我们组建了一个团队， 计算药物发现、机器学习、药物筛选和药物化学方面的专家。一是 将开发一个强大的，高性能的，可推广的数据驱动的化学结构嵌入方法 以增强药物诱导的基因表达预测。有了预测的配置文件，我们将部署RGES来评分 化合物用于给定的疾病概况。我们将评估筛选肝脏化合物的性能 癌症抑制剂、SARS-CoV-2抑制剂和细胞重编程调节剂。最后，我们将其应用于铅 优化.我们以前的药物再利用工作确定并验证了两个候选者：肝脏中的氯硝柳胺 DIPG中的癌症和霉酚酸。然而，氯硝柳胺的溶解性差和氯硝柳胺的渗透性差， 大脑中的麦考酚酸阻碍了它们的进一步发育。因此，我们将深入开展 强化学习框架来实现这两种药物的优化。与此同时，领域专家将 提出新的类似物。我们将综合类比并比较领域专家之间的性能 AI模型。我们希望这项工作将释放药物发现中新兴组学数据的力量。

PROJECT SUMMARY Today’s technologies allow profiling thousands of gene expression features for diseases and drugs at a very low cost. This proposal entitled “Virtual Compound Screening Using Gene Expression” aims to develop novel data science approaches to leverage emerging gene expression profiles to discover novel drugs. Previously, we developed a scoring function called RGES to quantify the drug’s potency to reverse disease gene expression based on the drug- and disease- expression profiles. We observed that RGES correlates with drug efficacy. Using this idea, we and others identified drugs that could be repurposed to treat a number of diseases. However, this approach currently does not support novel compound screening or lead optimization. To implement this approach for large-scale screening of a big compound library, we first need to generate gene expression profiles of the library compounds. However, because of the lack of large-scale gene expression profiles of new compounds, virtual compound screening was impossible until recent efforts including ours demonstrated the feasibility of predicting gene expression solely based on chemical structure. The objective of this project is thus to develop novel machine learning methods to boost the performance of drug-gene expression prediction and utilize the predicted profiles in practical drug discovery. To achieve the goals, we have assembled a team of experts in computational drug discovery, machine learning, drug screening, and medicinal chemistry. First, we will develop a robust, high-performance, and generalizable data-driven chemical structure embedding method to enhance drug-induced gene expression prediction. With the predicted profiles, we will deploy RGES to score compounds for given disease profiles. We will evaluate the performance in the screening of compounds for liver cancer inhibitors, SARS-CoV-2 inhibitors, and cell reprogramming regulators. Finally, we will apply it to lead optimization. Our previous drug repurposing efforts identified and validated two candidates: niclosamide in liver cancer and Mycophenolic acid in DIPG. However, the poor solubility of niclosamide and the poor penetration of Mycophenolic acid in the brain hindered their further development. Accordingly, we will develop a deep reinforcement learning framework to achieve the optimization of these two drugs. In parallel, domain experts will propose new analogs. We will synthesize the analogs and compare the performance between domain experts and the AI model. We expect this work will unleash the power of the emerging omics data in drug discovery.