Prof. Yasubumi Sakakibara
Keio University, Japan
Prof. Yasubumi Sakakibara is a Professor at the Department of Biosciences and Informatics, Keio University, Japan. He received his degree of Doctor of Science from Tokyo Institute of Technology. He spent one year as postdoc at UC Santa Cruz and collaborated with Prof. David Haussler on the project of stochastic context-free grammars for modeling RNAs. He also worked for Fujitsu Laboratory. His research interests are Bioinformatics, working on research that applies machine learning to omics data such as genomic data. This includes cancer genome analysis, gut microbiome analysis, drug discovery through virtual screening, and medical image analysis using deep learning.
Speech Title: "Deep Generative Model for Constructing Chemical Latent Space to Design and Discover Novel Drug Structures"
Abstract: The structural diversity of chemical libraries, which are systematic collections of compounds that have potential to bind to biomolecules, can be represented by chemical latent space. A chemical latent space is a projection of a compound structure into a mathematical space based on several molecular features, and it can express structural diversity within a compound library in order to explore a broader chemical space and generate novel compound structures for drug candidates. In this talk, we introduce a deep-learning method, called NP-VAE, based on variational autoencoder for managing hard-to-analyze datasets from DrugBank and large molecular structures such as natural compounds with chirality, an essential factor in the 3D complexity of compounds. NP-VAE was successful in constructing the chemical latent space from large-sized compounds that were unable to be handled in existing methods, achieving higher reconstruction accuracy, and demonstrating stable performance as a generative model across various indices. Furthermore, by exploring the acquired latent space, we succeeded in comprehensively analyzing a compound library containing natural compounds and generating novel compound structures with optimized functions.
Chair Prof. Ying Xu (AAAS Fellow and IEEE Fellow)
Southern University of Science and Technology, China
Ying Xu is a Chair Professor in the School of Medicine, Southern University of Science and Technology, China since January 2023. He is also a Cheungkong Scholar Chair Professor (2008 - ) and Qianren Chair Professor (2012 - ). Before that, he was a Regent Professor and the Georgia Research Alliance Eminent Scholar Chair in the Department of Biochemistry and Molecular Biology, the University of Georgia (2003 – 2022/12) and the Founding Director of the Institute of Bioinformatics, the University of Georgia, USA (2003-2011). He is an AAAS Fellow and an IEEE Fellow. He has been a computational biologist since 1993 when he joined the Oak Ridge National Laboratory to take part in the Human Genome Project, where he worked for ten years and moved up the career ladder from a research associate to a senior staff scientist and group leader. He has published over 400 research papers and five books, including the world's first monograph “Cancer Bioinformatics”. His H-Index is 71 with ~20000 citations in scholar.google. He received his Ph.D. in theoretical computer science from the University of Colorado in 1991 and earlier degrees from Jilin University, China.
Speech Title: "Studies of Disease Biology Need New Theoretical Frameworks"
Abstract: Chronical diseases, such as cancer, Alzheimer’s disease, and diabetes, generally have considerable changes in their cellular chemical conditions such as the pH and the O2 level, which result in changes in the cellular physical conditions, including the membrane potential and the intracellular polarity. These changes will profoundly alter the kinetics and thermodynamics of cellular chemical reactions, leading to the so-called metabolic reprogramming (MR) at a systems level, created by stress-induced genetic mutations and epigenetic alterations for survival. The affected cells may have to make drastic and fundamental changes, such as the significant simplification of their polarity system (which defines what a cell can and cannot do) via mutations as in cancer, to generate sustained metabolic exits for the newly created MRs, which give rise to specific phenotypes of a disease. To elucidate the operating logic of each disease type, or specifically how biological functions encoded in our genome behave in a fundamentally novel physicochemical microenvironment, we need to have a new research framework, which requires, at least, consideration of biology at the basic chemistry level.