Staff Profile: Gregory J. Tawa

Gregory J. Tawa
Gregory J. Tawa, Ph.D.

Modeling and Informatics Lead

Division of Preclinical Innovation
Therapeutic Development Branch

National Center for Advancing Translational Sciences

National Institutes of Health

Email Gregory J. Tawa

Biography

Gregory Tawa joined NCATS in 2014 as the modeling and informatics lead for the Therapeutic Development Branch (TDB) of the Center’s Division of Preclinical Innovation (DPI). In addition to supporting TDB’s computational needs, Tawa also leads multiple projects that focus on bioinformatics-driven, comparative canine-human oncology.

Prior to joining NCATS, Tawa spent 10 years in industry, focusing on computational drug design and chemo-informatics. Eight of these years were spent at Wyeth Pharmaceuticals as a key team member on multiple drug-discovery projects, ranging from the very early exploratory stages to the actual development phase. During his tenure, he gained intimate familiarity with the computational aspects of drug discovery.

Tawa received his doctorate in chemical physics from New York University in 1990, where he studied the quantum mechanics of small molecules. He performed his postdoctoral work at the University of Minnesota, where his research focused on the dynamics of atom-molecule scattering. Since then, Tawa’s work has gravitated toward the life sciences and human health.

Research Topics

Tawa’s research interests focus on the design and use of computational and informatics methods to facilitate the drug-discovery process, from early stage development to clinical application. Tawa is a team contributor on multiple DPI projects, using a wide range of methods to achieve team goals. These methods include analyzing genomic data, virtual screening, structure-based design, quantum mechanics, homology modeling, molecular dynamics, quantitative structure–activity relationship modeling, artificial intelligence and statistics. Tawa also leads several projects on computational and informatics methods to analyze and interpret multi-omics data relevant to rare diseases and cancer. A major goal is understanding comparative oncology (i.e., finding regions in the genome relevant to a disease but also consistent between animals and humans), then using this common genomic space to find and validate new biomarkers to be used as either diagnostics or targets for therapeutic intervention.

Selected Publications

  1. Characterization of Chemically Induced Liver Injuries Using Gene Co-expression Modules
  2. Design, Synthesis, and Biological Evaluation of Quinazolin-4-One-Based Hydroxamic Acids as Dual PI3K/HDAC Inhibitors
  3. Overcoming Adaptive Therapy Resistance in AML by Targeting Immune Response Pathways
  4. Identification of 53 Compounds That Block Ebola Virus-Like Particle Entry via a Repurposing Screen of Approved Drugs
  5. Synergistic Drug Combination Effectively Blocks Ebola Virus Infection