Using Common Fund datasets for xenobiotic localization

项目摘要 亚细胞定位，如细胞核溶酶体和线粒体，具有巨大的潜力， 增强治疗分子的有效性，而不是随机分布在整个细胞中。 随着亚细胞定位的改善和浓度的提高，特异性分子可以被更好地定位。 有效且毒性较小，这通常是随机分布和非特异性定位的问题。 因此，了解特定分子的亚细胞分布和机制可以进一步 调节亚细胞功能障碍介导的疾病。异源生物质在亚细胞水平的定位具有 对多个进程产生深远影响。这项工作的总体目标是开发一种新的 具有计算工具的平台，用于特定的异生物质定位。拟议的工作将利用 三个共同基金数据集。在具体的aim-1中，我们的目标是开发一套机器学习（ML）模型， 微区室化的分级水平和40个特定的亚细胞位置。这些机器学习 模型将首先使用三种不同类型的特征（基于指纹，基于药效团， 基于物理化学振荡器）。然后，他们融合使用先进的多层组合融合 算法，以获得最佳的共识模型。我们还将进行支架分析，以确定关键的 在特定亚细胞位置积累分子中发挥作用的支架。在具体目标2中，我们将 对ML模型的预测进行实验验证。具体来说，我们将测试50个 化合物的亚细胞位置。在具体目标3中，我们计划建立一个开放的门户网站， 数据集、ML模型、预测服务器和文档。项目生成的所有数据和模型 都是开源的。

Project Summary Subcellular localization, such as the nucleus lysosomes, and mitochondria, has tremendous potential to enhance the effectiveness of the therapeutic molecules rather than random distribution throughout the cell. With improved subcellular localization and enhanced concentration, a specific molecule can be more efficacious as well as less toxic which is usually a concern of random distribution and nonspecific localization. Therefore, understanding subcellular distribution and the mechanism for a specific molecule can further modulate subcellular dysfunction mediated diseases. Xenobiotic localization at the subcellular level has a profound effect on several processes. The overarching goal of the proposed work is to develop a novel platform with computational tools for specific xenobiotic localization. The proposed work will take advantage of three common fund datasets. In specific aim-1, we aim to develop a suite of machine learning (ML) models for hierarchical levels of micro-compartmentation and 40 specific subcellular locations. These machine learning models will be first built using three different types of features (fingerprints-based, pharmacophore-based, and physicochemical descriptors-based). Then, they are fused using an advanced multilayer combinatorial fusion algorithm to get the best consensus model. We will also perform the scaffold analysis to identify critical scaffolds that play a role in accumulating molecules at specific subcellular locations. In specific aim-2, we will conduct experimental validation of the predictions developed ML models. More specifically we will test 50 compounds for their subcellular location. In specific aim-3, we plan to build an open portal that incorporates datasets, ML model, prediction server, and documentation. All the data and models generated from the project are made available as open-source.