Graphs (or networks) are ubiquitous representations in life sciences and medicine, from molecular interactions maps, signaling transduction pathways, to graphs of scientific knowledge, and patient-disease-intervention relationships derived from population studies and/or real-world evidences. Recent advance in graph machine learning (ML) approaches such as graph neural networks (GNNs) has transformed a diverse set of problems relying on biomedical networks that traditionally depend on descriptive topological data analyses.
In this workshop, we will first provide a comprehensive overview of the types of biomedical graphs/networks, the underlying biological and medical problems, and the applications of graph ML algorithms for solving those problems important for drug discovery. Next, we will showcase four concrete GNN solutions in drug discovery: 1) training and fine-tuning GNN models for small-molecule property prediction on atomic graphs, 2) macro-molecule property and function prediction on residue graphs, 3) bi-graph based binding affinity prediction for protein-ligand pairs, and 4) organizing and generating new knowledge for drug discovery and repurposing with knowledge graphs.Bios:
Zichen Wang: Zichen is an Applied Scientist at AWS Machine Learning Solutions Lab where he developed various GNN applications for AWS customers in healthcare and life sciences. He received Ph.D. degree in Computational Biology from Icahn School of Medicine at Mount Sinai in New York, NY, USA in 2016. He continued his research in biomedical networks, systems pharmacology, and machine learning as a postdoctoral fellow and a research-track Assistant Professor at Mount Sinai. He has made contributions in many areas of biomedical informatics including bioinformatics software development, drug discovery, functional genomics, clinical informatics, and protein function prediction.
Vassilis N. Ioannidis: Vassilis is an Applied Scientist in AWS AI Research and Education (AIRE). He received his Diploma in Electrical and Computer Engineering from the National Technical University of Athens, Greece, in 2015, and the M.Sc. degree and Ph.D. degree in Electrical Engineering from the University of Minnesota (UMN), Twin Cities, Minneapolis, MN, USA, in 2017 and 2020 respectively. Vassilis received the Doctoral Dissertation Fellowship (2019-2020) from the University of Minnesota as a recognition for his research in graph neural networks (GNNs). He worked from June to Dec. 2019 at Mitsubishi Electric research labs in graph representation learning for point cloud processing. Since February 2020, he is working at AWS as an applied scientist in the deep graph library team, where he develops GNN solutions and performs research in GNNs.
Tatsuya Arai: Tatsuya is a Senior Research Scientist at Amazon Machine Learning Solutions Lab. He received Ph. D. degree in Bioengineering from the University of California, San Diego. His research interests are in the fields of quantitative functional medical imaging, image guided radiation therapy, and application of machine learning on the medical imaging.
