Using Common Fund datasets for xenobiotic localization

项目总结

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.