virtual compound screening using gene expression

项目总结 今天的技术允许以非常低的速度对疾病和药物的数千个基因表达特征进行分析 成本。这项题为“利用基因表达进行虚拟化合物筛选”的提议旨在开发新的数据 利用新兴的基因表达谱来发现新药的科学方法。此前，我们 开发了一种名为RGES的评分函数来量化药物逆转疾病基因表达的效力 根据药物和疾病的表达特征。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被重新用于治疗多种疾病的药物。然而， 这种方法目前不支持新的化合物筛选或前导优化。要实现这一点， 对于大规模筛选大型化合物文库的方法，首先需要生成基因表达谱 图书馆的化合物。然而，由于缺乏大规模的新的基因表达谱 化合物，虚拟化合物筛选是不可能的，直到包括我们在内的最近的努力证明 仅根据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和预测的性能 在实际的药物发现中利用预测的轮廓。为了实现这些目标，我们组建了一支 计算机药物发现、机器学习、药物筛选和药物化学方面的专家。首先，我们 将开发一种健壮、高性能和可推广的数据驱动的化学结构嵌入方法 加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RGE来得分 针对给定疾病特征的化合物。我们将评估其在肝脏化合物筛选中的性能。 癌症抑制剂、SARS-CoV-2抑制剂和细胞重新编程调节剂。最后，我们将其应用于Lead 优化。我们先前的药物再利用工作确定并验证了两个候选药物：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，透皮性能差。 大脑中的霉酚酸阻碍了它们的进一步发育。据此，我们将发展一种深层次的 强化学习框架实现了对这两种药物的优化。同时，领域专家将 提出新的类比。我们将综合这些类比并比较领域专家之间的性能 以及人工智能模型。我们预计这项工作将释放新兴组学数据在药物发现中的力量。

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.