Graph Data Modeling: Molecules, Proteins, & Chemical Processes

José Manuel Barraza-Chavez is a Ph.D. candidate in the Department of Chemical Engineering & Applied Chemistry at the University of Toronto. His research spans the integration of machine learning, engineering and laboratory automation to accelerate discovery across chemical and biological systems. Currently, he is developing data-driven models and self-driving experimental workflows that bridge computation with hands-on engineering. He began his journey in biotechnology by receiving his BASc in Biochemical Engineering from the Autonomous University of Sinaloa in 2023 and continues to combine broad scientific curiosity with practical problem-solving.

Rana A. Barghout is a Ph.D. candidate at the University of Toronto’s Chemical Engineering & Applied Chemistry Department. Her research studies the use of machine learning for advancing biological modeling at different scales, from proteins and molecules to metabolic networks. She is currently working on applying deep learning to large scale metabolic optimization problems. She received her BASc in Chemical and Biological Engineering from the University of British Columbia in 2021 where her journey with ML for biological modeling began.

Ricardo Almada-Monter is a Ph.D. student in the Department of Chemistry and Biochemistry at the University of California, San Diego. His research focuses on integrating Large Language Models with Molecular Dynamics Simulations to predict the biophysical properties of enzymes and protein−protein interactions. Before joining UC San Diego, Ricardo conducted research on Quantitative Structure Activity Relationships (QSAR) for copper complexes using quantum chemical descriptors. He holds an M.Sc. in Theoretical Chemistry from the University of Chicago and a B.Sc. in Chemistry from the National Autonomous University of Mexico (UNAM).

Benjamin Sanchez-Lengeling is an Assistant Professor of Chemical Engineering at the University of Toronto, where he leads the Chemical Cognition Lab. After five years as a Research Scientist at Google Deepmind/Brain, he now focuses on designing and building data-driven computational tools to accelerate molecular discovery. His research spans small molecules, polymers, and proteins, with a strong emphasis on translating computational predictions into practice through collaborative experimental validation and interpretable models. Beyond his research, Benjamin co-founded the STEM-education NGO Clubes de Ciencia México and the LatinX-centered AI conference RIIAA to broaden participation in science and technology.

Adrian Jinich is a Mexico City born chemical biologist and HHMI Hanna Gray Fellow who combines machine learning, metabolomics, and structural modeling to uncover unannotated enzymes in Mycobacterium tuberculosis and other pathogens. Trained at Harvard (Ph.D.) and Weill Cornell (post-doc), he now leads computational experimental efforts that aim to translate hidden metabolic functions into new antibacterial strategies at the University of California, San Diego. Adrián co-founded Clubes de Ciencia México to expand STEM opportunities across Latin America and, outside the lab, enjoys exploring trees and insects with his two children.

Radhakrishnan Mahadevan expertise is in systems biology, synthetic biology, and metabolic modeling and engineering. He is Professor of Chemical Engineering & Applied Chemistry department at the University of Toronto and holds the Canada Research Chair in Metabolic Systems Engineering. His group blends computational and experimental methods to analyze, engineer, and control microbial metabolism, advancing applications from bioremediation to the sustainable biosynthesis of high-value chemicals. He received a Ph.D. in Chemical Engineering from the University of Delaware in 2002, after a B.Tech. in Chemical Engineering from the Indian Institute of Technology Madras in 1997.