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virtual compound screening using gene expression

使用基因表达进行虚拟化合物筛选

基本信息

批准号：
10673837
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
项目类别：
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/10673837
关键词：
2019-nCoV Address Advanced Development Artificial Intelligence Biological Biological Assay Brain Case Study Cell Line Cell Reprogramming 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 Learning Libraries Liver Fibrosis Machine Learning Magic Malignant neoplasm of liver Medicine 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 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 therapeutic candidate virtual

项目摘要

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.

项目总结