Learning How to Give Casual Explanations for Large Scale Virtual and Morphological Pharmacology

学习如何对大规模虚拟和形态药理学进行随意解释

• 批准号: 10713386
• 负责人: Matthew J O'Meara
• 金额: $ 38.19万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10713386
• 关键词: 3-Dimensional; Binding Sites; Biological; Biological Models; Biotechnology; Cell Communication; Cell model; Cells; Cellular Morphology; Chemicals; Complex; Cultured Cells; Development; Environment; Ethics; Failure; Fluorescent Dyes; Individual; Learning; Libraries; Ligand Binding; Ligands; Maps; Methods; Microscope; Modeling; Molecular; Morphology; Organoids; Pharmacology; Research; Research Personnel; Robotics; Selection Bias; Statistical Data Interpretation; Statistical Methods; System; Training; Translations; Work; biological systems; cost; deep learning model; drug discovery; high dimensionality; improved; interest; machine learning method; new technology; programs; response; screening; simulation; three dimensional cell culture; timeline; virtual; virtual screening

To unravel the complexity of biological systems researchers have traditionally studied reductive model systems like cultured cells or simple molecular simulations. While these reductive model systems can be cheaper, easier, and/or more ethical to manipulate, findings in them may not translate to the biological systems of primary interest. This is especially important for drug-discovery, as late-stage failures result in enormous costs and long development timelines. Excitingly, recent advances in biotechnology and computing have made more complex model systems—including 3D organoids and large-scale virtual screening—more tractable. However, an emerging challenge is that standard statistical methods developed to analyze simple model systems are insufficient to analyze these more complex model systems. Complex model systems are inherently heterogeneous. The key statistical challenge is to leverage the higher dimensional readouts afforded by the new technologies to identify the causal mechanisms relevant for translation. When done properly, better statistical analysis can unlock the potential of new technology to better represent target biological systems with more precision and less bias. The overarching theme of my research program is to develop causal inference methods for complex model systems for pharmacology. Complex systems analyze in my group include morphological profiling, where robotic confocal microscopes with multiplexed fluorescent dyes are used to rapidly characterize the rich cellular morphological of individual cells, and large-scale virtual screening, where molecular simulations are used prioritize compounds from make-on-demand libraries containing tens of billions of molecules. We draw parallels across these distinct screening platforms, we develop and apply causal inference methods to better guide translatable discoveries. Project one: Account for spatial call-to-environment and cell-to-cell interactions in morphological profiling of organoids in 3D culture. Depending on the downstream application, spatial factors can either define or confound relevant biological responses. We will develop global and local models for cellular spatial factors and use them as statistical controls while avoiding selection bias to model the effects of chemical perturbations. Project two: Mapping bioactive chemical space for adaptive large-scale virtual screening. AI guided synthesis prediction is rapidly open new chemical spaces for virtual screening. However, it is not clear how to take advantage of the increased chemical diversity to best improve target specific or selectivity. We propose to train high-capacity deep-learning models to represent compounds based their compatibility with ligand binding sites. This chemical-space map will enable characterizing how perturbations to virtual screening binding sites and simulation methods effect the distribution of predicted ligands.

为了揭示生物系统的复杂性，研究人员传统上研究还原模型 像培养细胞或简单的分子模拟系统。虽然这些简化模型系统可以 更便宜，更容易和/或更道德的操作，其中的发现可能不会转化为生物系统 最重要的。这对药物发现尤其重要，因为后期失败会导致巨大的风险。 成本和长期的开发时间。令人兴奋的是，生物技术和计算技术的最新进展使 更复杂的模型系统-包括3D类器官和大规模虚拟屏幕-更易于处理。 然而，一个新出现的挑战是，标准的统计方法开发来分析简单的模型， 系统不足以分析这些更复杂的模型系统。复杂的模型系统是 本质上是异质的。关键的统计挑战是利用提供的更高维读数 通过新技术来识别与翻译相关的因果机制。如果做得好， 统计分析可以释放新技术的潜力，以更好地代表目标生物系统， 更精确，更少偏差。 我的研究计划的首要主题是开发因果推理方法， 药理学的模型系统我的小组分析复杂系统包括形态学分析， 其中具有多重荧光染料的机器人共焦显微镜用于快速表征富 单个细胞的细胞形态学，以及大规模的虚拟筛选，其中分子模拟是 使用者从包含数百亿分子的按需制造库中优先选择化合物。我们提请 在这些不同的筛选平台的平行，我们开发和应用因果推理方法，以更好地 引导可翻译的发现。 项目一：在形态学中解释空间对环境的呼唤和细胞对细胞的相互作用 在3D培养中分析类器官。根据下游应用，空间因素可以定义 或混淆相关的生物反应。我们将开发细胞空间因素的全球和局部模型 并将其用作统计控制，同时避免选择偏差，以模拟化学物质的影响， 扰动 项目二：绘制生物活性化学空间用于适应性大规模虚拟筛选。AI引导 合成预测为虚拟筛选迅速打开新化学空间。然而，目前尚不清楚如何 利用增加的化学多样性来最好地改进目标特异性或选择性。我们建议 训练高容量深度学习模型，以基于化合物与配体结合的相容性来表示化合物 网站.这种化学空间图将能够表征如何扰动虚拟筛选结合位点 模拟方法影响预测配体的分布。