Biography

Jessica Maine, Ph.D., is a staff scientist in the Informatics (IFX) Core of NCATS’ Division of Preclinical Innovation. She has a strong background in neurodegenerative molecular biology and expertise as a data scientist. She leads the development of biomedical graph databases and machine-learning models for drug discovery and biomarker identification. In the IFX Core, Maine helps design a modular data ecosystem, focusing on curating, harmonizing and standardizing biomedical data, while integrating molecular entities, pathways, networks and experimental data to support translational research. This work enables insights like computational drug repositioning and developing machine-learning models to predict drug targets and disease-associated genes.

Before joining NCATS, Maine was a senior investigator at Roivant Sciences. She applied her molecular biology skills and worked on the company’s computational system. The system combines cheminformatics, computational physics, artificial intelligence and translational informatics to speed drug discovery. Maine also was a senior research scientist at Nature’s Toolbox, Inc. She led the production of in-house proprietary vectors for Escherichia coli protein expression and optimized cell-free biomanufacturing systems.

While earning her doctorate in biomedical science from The University of New Mexico, Maine designed new optogenetic tools. She also researched Alzheimer’s disease genes using induced pluripotent stem cells. Maine holds a Bachelor of Science in biochemistry from the same institution. For her undergraduate studies, she genetically modified Candida albicans to characterize virulence.

Research Topics

Maine focuses on applying biomedical multi-omics to support translational research in drug discovery and rare diseases. She promotes standardized ontologies for FAIR principles to ensure data are findable, accessible, interoperable and reusable. FAIR principles are key to high-quality knowledge graphs that capture semantic relationships between biomedical entities and improve models for machine learning.