virtual compound screening using gene expression

使用基因表达进行虚拟化合物筛选

• 批准号: 10418186
• 负责人: Bin Chen
• 金额: $ 42.08万
• 依托单位: MICHIGAN STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10418186
• 关键词: 2019-nCoV; Address; Advanced Development; Artificial Intelligence; Biological; Biological Assay; Brain; Case Study; Cell Line; Cells; Chemical Structure; Data; Data Science; Data Set; Development; Diffuse intrinsic pontine glioma; Disease; Drug Screening; Fingerprint; Gene Expression; Gene Expression Profile; Generations; Genes; Goals; Graph; Heterogeneity; In Vitro; Knowledge; Label; Lead; Learning; Libraries; Liver Fibrosis; Machine Learning; Magic; Malignant neoplasm of liver; Methods; Modeling; Molecular; Molecular Disease; Mycophenolic Acid; Penetration; Performance; Pharmaceutical Chemistry; Pharmaceutical Preparations; Pre-Clinical Model; Primary carcinoma of the liver cells; Property; Proteins; Publishing; SARS-CoV-2 inhibitor; Scientist; Solubility; Structure; Technology; Testing; Toxic effect; Training; Work; analog; base; computer framework; cost; deep learning model; deep reinforcement learning; drug discovery; drug efficacy; drug repurposing; experience; genetic signature; graph neural network; improved; inhibitor; lead optimization; machine learning method; machine learning model; multi-task learning; novel; novel therapeutics; overexpression; screening; small molecule; virtual

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.

项目摘要 当今的技术允许分析数千种疾病和药物的基因表达特征 成本。该建议标题为“使用基因表达的虚拟化合物筛选”旨在开发新的数据 科学方法利用新兴的基因表达谱发现新药。以前，我们 开发了一种称为RGE的评分功能，以量化药物逆转疾病基因表达的效力 基于药物和疾病表达谱。我们观察到RES与药物效率相关。 使用这个想法，我们和其他人确定了可以重新使用以治疗多种疾病的药物。然而， 目前，这种方法不支持新颖的化合物筛选或铅优化。实施此功能 大规模筛选大型复合库的方法，我们首先需要生成基因表达曲线 图书馆化合物。但是，由于缺乏新的基因表达曲线 化合物，虚拟化合物筛选是不可能的 仅基于化学结构预测基因表达的可行性。因此，这个项目的目的是 开发新颖的机器学习方法，以提高药物 - 基因表达预测的性能和 在实用药物发现中利用预测的概况。为了实现目标，我们组建了一个团队 计算药物发现，机器学习，药物筛查和医学化学专家。首先，我们 将开发出强大，高性能且可推广的数据驱动的化学结构嵌入方法 增强药物诱导的基因表达预测。使用预测的配置文件，我们将部署RES来得分 给定疾病特征的化合物。我们将评估筛查肝脏化合物的性能 癌症抑制剂，SARS-COV-2抑制剂和细胞重编程调节剂。最后，我们将其应用于领导 优化。我们以前的药物重新利用努力确定并证实了两名候选者：肝脏中的烟酰胺 DIPG中的癌症和霉酚酸。但是，烟酰胺的溶解度差和渗透率不佳 大脑中的霉酚酸阻碍了他们的进一步发展。根据，我们将发展一个深刻的 加强学习框架以实现这两种药物的优化。同时，领域专家将 提案新类似物。我们将综合模拟并比较域专家之间的性能 和AI模型。我们预计这项工作不会牵涉到药物发现中新兴的OMICS数据的力量。