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了减少模型 诸如培养细胞或简单分子模拟之类的系统。尽管这些减少的模型系统可以是 更便宜，更容易和/或更合乎道德的操纵，其中的发现可能不会转化为生物系统 主要兴趣。这对于药物发现尤其重要，因为后期失败导致巨大 成本和较长的开发时间表。令人兴奋的是，生物技术和计算的最新进展已取得 更复杂的模型系统（包括3D器官和大规模虚拟筛选）更容易处理。 但是，新出现的挑战是开发了用于分析简单模型的标准统计方法 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 天生的异质。关键的统计挑战是利用提供的更高维度的读数 通过新技术来确定与翻译相关的因果机制。正确完成后，更好 统计分析可以释放新技术更好地代表目标生物系统的潜力 更精确，更少的偏见。 我的研究计划的总体主题是开发复杂的因果推理方法 药理学模型系统。我小组中的复杂系统分析包括形态分析， 其中使用具有多重荧光染料的机器人共聚焦显微镜来快速表征富含的 单个细胞的细胞形态和大规模的虚拟筛选，其中分子模拟为 使用的优先级化合物来自包含数以千十亿个分子的需求库的化合物。我们画 我们在这些不同的筛选平台上相似，我们开发和应用因果推理方法来更好 指南可翻译的发现。 项目一：形态学中的空间召唤环境和细胞对细胞相互作用的帐户 在3D培养中的类器官的分析。根据下游应用的不同，空间因素可以定义 或混淆相关的生物反应。我们将开发用于细胞空间因素的全球和本地模型 并将它们用作统计控制，同时避免选择偏差来建模化学的影响 扰动。 项目第二：用于自适应大规模虚拟筛查的生物活性化学空间。 AI指导 合成预测是用于虚拟筛查的迅速开放的新化学空间。但是，尚不清楚如何 利用增加的化学多样性，以最好地提高目标特异性或选择性。我们建议 训练高容量的深度学习模型，以代表化合物基于其与配体结合的兼容性 站点。该化学空间图将使您表征对虚拟筛选结合位点的扰动 模拟方法影响预测配体的分布。