Smart Diaphragm

Invented a simple diaphragm-like sensor to detect pre-term labor using wireless connections to alert medical caregivers.


The Smart Diaphragm may drastically reduce the leading cause of infant mortality (15 million premature births globally) and decrease the societal cost of $26 million worldwide.

Principal faculty

  • Shuvo Roy, PhD, with PhD student Mozziyar Etemadi


Nanotechnology for macular degeneration therapy

Created advanced micro- and nano-biosystems in the design and fabrication of a tiny, flexible, implantable film that delivers conventional medicine and complex antibody-based drugs for retinal disease therapies.


Ocular disease treatments may require painful monthly eye injections. The application of nanotechology can reduce injections and lower patients’ financial burdens while maximizing the therapeutic effect of drugs.

Principal faculty

  • Tejal Desai, PhD

High-throughput BioLab

Developed microfluidic systems for performing biological experiments with microdroplets.


Microfluidic methods can reduce the speed and size of experimental components (“test tube” containers) needed for processing large numbers of reactions, thus lowering the cost and time required for screening drug candidate molecules.

Principal faculty

  • Adam Abate, PhD


Biopharmaceutics Drug Disposition Classification System (BDDCS)

Created a roadmap for predicting enzyme and transporter interactions for different classes of new molecular entities.


Predicts drug absorption and disposition as well as potential drug-drug interactions not tested in the drug approval process; identifies previously unexplained effects of accumulation of substances in renal failure; provides a new tool for screening and predicting drug side effects.

Principal faculty

  • Leslie Benet, PhD


Structure Function Linkage Database (SFLD)

Built the first catalogue of complete protein sequences, their structures, related functions, and similarity networks, including the classification of superfamily members into subgroups and families.


Provides researchers with a database to search for specific enzymes, to browse reactions, and to identify potential misannotations of enzyme functions—all leading to a better understanding of biological systems and therapeutic targets.

Principal faculty

  • Patricia Babbitt, PhD


Pharmacogenomics Research Network and Center for Genomic Medicine (PGRN-CGM)

Advanced a collaborative effort of the NIH Pharmacogenomics Research Network and Japan’s RIKEN Center for Genomic Medicine, seeking to identify genetic variants that contribute to individual response to medicines using genome-wide approaches. This program was first known as Global Alliance for Pharmacogenomics, Japan (GAP-J).


Helps clinicians to optimize the safety and effectiveness of drugs for each patient and consolidates the push toward personalized medicine on a global level.

Principal faculty

  • Kathy Giacomini, PhD


Ethnicity, genetic ancestry, and asthma origins

Developed the largest and most ethnically diverse asthma study in the United States.


Understanding the role of genetics, ancestry, and the environment in asthma patients’ therapeutic responses allows the medical practitioner to adapt dosages for improved outcomes and to address asthma health disparities. The study also establishes the role of genetics in drug response.

Principal faculty

  • Esteban G. Burchard, MD, MPH


Computational tools for small-molecule molecular modeling: the Surflex Platform

Developed methods for small molecule docking, molecular similarity, and binding affinity prediction that rely upon ideas from computer science and robotics, including representation of deformable objects, multiple-instance learning, and heuristic non-linear optimization. These methods are widely used in industry and academia for rational drug design.

Principal faculty

  • Ajay Jain, PhD


Computational tools for comparative protein structure modeling: MODELLER, ModBase, IMP

Developed a powerful suite of computational tools for constructing comparative protein structures using statistical models to fill in the gaps created by unknown loop structures. These tools include:

  • MODELLER: used for homology (comparative modeling of three-dimensional protein structures)
  • ModBase: database of comparative protein structure models
  • IMP (Integrative Modeling Platform): software for hybrid determination of macromolecular assembly structures


Assists in prediction of protein-protein interactions and the functional annotation of genes, allowing the identification of specific proteins as therapeutic targets.

Principal faculty

  • Andrej Sali, PhD


Population pharmacokinetic modeling: Non-linear Mixed Effects Modeling (NONMEM)

Created the first software for population pharmacokinetic modeling, now the gold standard in the field for both the pharmaceutical industry and academia. This work allowed the prediction of drug variability—its sources and its effects in individuals—based on sparse data.


Modeling tools can drastically reduce the number of clinical trial runs needed, abbreviating drug approval times and shrinking drug development costs.

Principal faculty

  • Stuart Beal, PhD
  • Lewis Sheiner, MD