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:
  • 金额:
    $ 42.08万
  • 依托单位:
  • 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

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

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

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Bin Chen其他文献

Bin Chen的其他文献

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{{ truncateString('Bin Chen', 18)}}的其他基金

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
  • 财政年份:
    2022
  • 资助金额:
    $ 42.08万
  • 项目类别:
Equipment Purchases for R01GM145700
R01GM145700 的设备采购
  • 批准号:
    10795418
  • 财政年份:
    2022
  • 资助金额:
    $ 42.08万
  • 项目类别:
A postdoctoral training program for impactful careers in stem cell biology
干细胞生物学领域有影响力的职业博士后培训计划
  • 批准号:
    10592329
  • 财政年份:
    2022
  • 资助金额:
    $ 42.08万
  • 项目类别:
Drug biomarker resources for precise translational research
用于精准转化研究的药物生物标志物资源
  • 批准号:
    10056488
  • 财政年份:
    2020
  • 资助金额:
    $ 42.08万
  • 项目类别:
Repurpose open data to discover therapeutics for understudied diseases
重新利用开放数据来发现尚未研究的疾病的治疗方法
  • 批准号:
    10461787
  • 财政年份:
    2019
  • 资助金额:
    $ 42.08万
  • 项目类别:
Repurpose open data to discover therapeutics for understudied diseases
重新利用开放数据来发现尚未研究的疾病的治疗方法
  • 批准号:
    10704561
  • 财政年份:
    2019
  • 资助金额:
    $ 42.08万
  • 项目类别:
Repurpose open data to discover therapeutics for understudied diseases
重新利用开放数据来发现尚未研究的疾病的治疗方法
  • 批准号:
    10669357
  • 财政年份:
    2019
  • 资助金额:
    $ 42.08万
  • 项目类别:
Repurpose open data to discover therapeutics for understudied diseases
重新利用开放数据来发现尚未研究的疾病的治疗方法
  • 批准号:
    10713005
  • 财政年份:
    2019
  • 资助金额:
    $ 42.08万
  • 项目类别:
Repurpose open data to discover therapeutics for understudied diseases
重新利用开放数据来发现尚未研究的疾病的治疗方法
  • 批准号:
    10231115
  • 财政年份:
    2019
  • 资助金额:
    $ 42.08万
  • 项目类别:
Integrating transcriptomic, proteomic and pharmacogenomic data to inform individualized therapy in cancers
整合转录组学、蛋白质组学和药物基因组学数据,为癌症个体化治疗提供信息
  • 批准号:
    9925076
  • 财政年份:
    2018
  • 资助金额:
    $ 42.08万
  • 项目类别:

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