Using Common Fund datasets for xenobiotic localization

项目摘要 亚细胞定位，如核溶酶体和线粒体，具有巨大的潜力 提高治疗分子的有效性，而不是在细胞内随机分布。 随着亚细胞定位的改善和浓度的提高，特定的分子可能会 有效且毒性小，这通常是一个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。异种生物在亚细胞水平上的定位有一个 对几个过程产生了深远的影响。拟议工作的首要目标是开发一部小说 具有计算工具的平台，用于特定的异种生物定位。拟议的工作将利用 三个常见的基金数据集。在特定的目标-1中，我们的目标是开发一套机器学习(ML)模型，用于 分层的微区隔和40个特定的亚细胞位置。这些机器学习 将首先使用三种不同类型的特征(基于指纹、基于药效团和 基于物理化学描述符)。然后，使用先进的多层组合融合技术对它们进行融合 算法得到最优的共识模型。我们还将执行脚手架分析，以确定关键 在特定的亚细胞位置起到聚集分子的作用的支架。在具体目标-2中，我们将 对所开发的ML模型的预测进行实验验证。更具体地说，我们将测试50 化合物的亚细胞位置。在具体的目标-3中，我们计划建立一个开放的门户网站，其中包括 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 都是以开源形式提供的。

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.