Mathematical AI for molecular data analysis

15^th November 2022, 13:00 Ashton Lecture Theatre
‪Dr. Kelin Xia
School of Physical and Mathematical Sciences, Nanyang Technological University

Abstract

Artificial intelligence (AI) based molecular data analysis has begun to gain momentum due to the great advancement in experimental data, computational power and learning models. However, a major issue that remains for all AI-based learning models is the efficient molecular representations and featurization. Here we propose advanced mathematics-based molecular representations and featurization (or feature engineering). Molecular structures and their interactions are represented as various simplicial complexes (Rips complex, Neighborhood complex, Dowker complex, and Hom-complex), hypergraphs, and Tor-algebra-based models. Molecular descriptors are systematically generated from various persistent invariants, including persistent homology, persistent Ricci curvature, persistent spectral, and persistent Tor-algebra. These features are combined with machine learning and deep learning models, including random forest, CNN, RNN, Transformer, BERT, and others. They have demonstrated great advantage over traditional models in drug design and material informatics.

Biography

Dr. Kelin Xia obtained his Ph.D. degree from the Chinese Academy of Sciences in Jan 2013. He was a visiting scholar in the department of Mathematics, Michigan State University from Dec 2009-Dec 2012. From Jan 2013 to May 2016, he worked as a visiting assistant professor at Michigan State University. He joined Nanyang Technological University at Jun 2016. His research focused on Mathematical AI for molecular sciences. He has published >60 papers and has been PI and Co-PI for 15 grants (>3.0M SGD).