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
- 来源:
- 关键词:2019-nCoVAddressAdvanced DevelopmentArtificial IntelligenceBiologicalBiological AssayBrainCase StudyCell LineCell ReprogrammingCellsChemical StructureDataData ScienceData SetDevelopmentDiffuse intrinsic pontine gliomaDiseaseDrug ScreeningFingerprintGene ExpressionGene Expression ProfileGenerationsGenesGoalsGraphHeterogeneityIn VitroKnowledgeLabelLearningLibrariesLiver FibrosisMachine LearningMagicMalignant neoplasm of liverMedicineMethodsModelingMolecularMolecular DiseaseMycophenolic AcidPenetrationPerformancePharmaceutical ChemistryPharmaceutical PreparationsPre-Clinical ModelPrimary carcinoma of the liver cellsPropertyProteinsPublishingSARS-CoV-2 inhibitorScientistSolubilityStructureTechnologyTestingToxic effectTrainingWorkanalogcomputer frameworkcostdeep learning modeldeep reinforcement learningdrug discoverydrug efficacydrug repurposingexperiencegenetic signaturegraph neural networkimprovedinhibitorlead optimizationmachine learning methodmachine learning modelmulti-task learningnovelnovel therapeuticsoverexpressionscreeningsmall moleculetherapeutic candidatevirtual
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。
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)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Bin Chen其他文献
Bin Chen的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ 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:
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:
相似海外基金
Rational design of rapidly translatable, highly antigenic and novel recombinant immunogens to address deficiencies of current snakebite treatments
合理设计可快速翻译、高抗原性和新型重组免疫原,以解决当前蛇咬伤治疗的缺陷
- 批准号:
MR/S03398X/2 - 财政年份:2024
- 资助金额:
$ 42.08万 - 项目类别:
Fellowship
Re-thinking drug nanocrystals as highly loaded vectors to address key unmet therapeutic challenges
重新思考药物纳米晶体作为高负载载体以解决关键的未满足的治疗挑战
- 批准号:
EP/Y001486/1 - 财政年份:2024
- 资助金额:
$ 42.08万 - 项目类别:
Research Grant
CAREER: FEAST (Food Ecosystems And circularity for Sustainable Transformation) framework to address Hidden Hunger
职业:FEAST(食品生态系统和可持续转型循环)框架解决隐性饥饿
- 批准号:
2338423 - 财政年份:2024
- 资助金额:
$ 42.08万 - 项目类别:
Continuing Grant
Metrology to address ion suppression in multimodal mass spectrometry imaging with application in oncology
计量学解决多模态质谱成像中的离子抑制问题及其在肿瘤学中的应用
- 批准号:
MR/X03657X/1 - 财政年份:2024
- 资助金额:
$ 42.08万 - 项目类别:
Fellowship
CRII: SHF: A Novel Address Translation Architecture for Virtualized Clouds
CRII:SHF:一种用于虚拟化云的新型地址转换架构
- 批准号:
2348066 - 财政年份:2024
- 资助金额:
$ 42.08万 - 项目类别:
Standard Grant
The Abundance Project: Enhancing Cultural & Green Inclusion in Social Prescribing in Southwest London to Address Ethnic Inequalities in Mental Health
丰富项目:增强文化
- 批准号:
AH/Z505481/1 - 财政年份:2024
- 资助金额:
$ 42.08万 - 项目类别:
Research Grant
ERAMET - Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
ERAMET - 快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
- 批准号:
10107647 - 财政年份:2024
- 资助金额:
$ 42.08万 - 项目类别:
EU-Funded
BIORETS: Convergence Research Experiences for Teachers in Synthetic and Systems Biology to Address Challenges in Food, Health, Energy, and Environment
BIORETS:合成和系统生物学教师的融合研究经验,以应对食品、健康、能源和环境方面的挑战
- 批准号:
2341402 - 财政年份:2024
- 资助金额:
$ 42.08万 - 项目类别:
Standard Grant
Ecosystem for rapid adoption of modelling and simulation METhods to address regulatory needs in the development of orphan and paediatric medicines
快速采用建模和模拟方法的生态系统,以满足孤儿药和儿科药物开发中的监管需求
- 批准号:
10106221 - 财政年份:2024
- 资助金额:
$ 42.08万 - 项目类别:
EU-Funded
Recite: Building Research by Communities to Address Inequities through Expression
背诵:社区开展研究,通过表达解决不平等问题
- 批准号:
AH/Z505341/1 - 财政年份:2024
- 资助金额:
$ 42.08万 - 项目类别:
Research Grant