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

为了揭开生物系统的复杂性，研究人员传统上研究的是还原模型 培养细胞或简单的分子模拟等系统。而这些简化的模型系统可以 更便宜、更容易和/或更合乎道德的操纵，这些发现可能不会转化为生物系统 主要利益所在。这对药物发现尤其重要，因为后期失败会导致巨大的 成本和较长的开发时间。令人兴奋的是，生物技术和计算机的最新进展已经取得了 更复杂的模型系统--包括3D有机体和大规模虚拟筛选--更容易处理。 然而，一个新出现的挑战是开发了标准的统计方法来分析简单的模型 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 天生的异质的。关键的统计挑战是利用所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。如果处理得当，效果会更好 统计分析可以释放新技术的潜力，更好地用 更高的精确度和更少的偏差。 我的研究计划的主要主题是开发复杂结构的因果推理方法 药理学的模型系统。在我的团队中分析的复杂系统包括形态分析， 机器人共焦显微镜和多路荧光染料被用来快速表征 单个细胞的细胞形态，以及大规模的虚拟筛选，其中分子模拟 用于对包含数百亿分子的按需制造库中的化合物进行优先排序。我们抽签 在这些不同的筛选平台上，我们开发并应用因果推理方法来更好地 引导可翻译的发现。 项目一：在形态学中解释对环境的空间呼唤和细胞与细胞的相互作用 3D培养中有机物质的剖析。根据下游应用，空间因素可以定义 或混淆相关的生物反应。我们将开发全球和局部的元胞空间因素模型 并使用它们作为统计对照，同时避免选择偏差来模拟化学物质的影响 微扰。 项目二：绘制生物活性化学空间图进行自适应大规模虚拟筛选。人工智能制导 合成预测为虚拟筛选开辟了新的化学空间。然而，目前还不清楚如何 利用增加的化学多样性来最大限度地提高目标的特异性或选择性。我们建议 训练大容量深度学习模型，根据化合物与配体结合的相容性来表示化合物 网站。这张化学空间图将能够表征对虚拟筛选结合位点的扰动 模拟方法会影响预测配体的分布。

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.