In this paper, we propose a novel metric to characterize drug molecules based on their interaction with genes, to tackle dimensionality challenges with the DrugMatrix toxicogenomics dataset. We developed a graph neural network (GNN) that is able to accurately predict this metric and produce informative graph-level vector representations that represent relative similarity between drug molecules, by capturing both structural and functional information of drug molecules. The GNN’s resulting embedding vector representations achieve better performance than both traditional fingerprint representations and the functional property data, in clustering tasks. With its demonstrated efficacy, there is potential for further advancements in the field of toxicogenomics and future applications of GNNs in high-dimensional data analysis.

This study aims to effectively analyze and visualize the concept to concept network derived from the COVID-19 Open Research Dataset (CORD-19) dataset, where we have more than 48,000 concepts with more than 300,000 relationships between concepts. In analyzing networks, we focus on finding relationship patterns between the coronavirus disease 2019 (COVID-19) concepts and other concepts. Given the node and edge datasets, we construct directional graphs and calculate all pair shortest paths based on multiple edge weight schemes. However, statistical metrics are not sufficient to identify specific relationships represented in the network. Therefore, we also propose a visual analytics approach to effectively understand the knowledge graph. Our highly interactive visual analytics allows users to effectively analyze the evolving graphs and (COVID-19) concept nodes and other nodes related to the COVID-19 nodes. We envision that this study will pave the path to develop strategies to provide more accurate and scalable predictive analysis on knowledge graphs related to CORD19 and other biomedical knowledge graphs.

Drug discovery is a costly process of searching for new candidate medications. Among its various stages, lead optimization easily consumes more than half of the pre-clinical budget. We propose an automated lead optimization workflow that uses data mining methods in components such as execution of molecular simulations, feature extraction, and clustering with convolutional variational autoencoder. The end-to-end execution produces protein-ligand binding affinity of atoms in the lead molecule which serves as metrics for identifying modifiable atoms. In contrast to known methods, our method provides new hints for drug modification hotspots which can be used to improve drug efficacy. Our workflow can potentially reduce the lead optimization turnaround time from months/years to several days compared with the conventional labor-intensive process and thus will become a valuable tool for medical researchers.