Therapeutic Drug Discovery
The Therapeutic Drug Discovery effort at the University of Maryland Institute for Health Computing is dedicated to addressing global and local health threats like cancer, heart disease, and diabetes.
- Driven by Artificial Chemical Intelligence: At the core of our efforts will be a combination of rigorous computational chemistry and Artificial Intelligence, christened together as Artificial Chemical Intelligence (ACI). This will enable screening through extremely large libraries of potential targets and drugs, accounting for target flexibility, pharmacokinetics and possible patient-specific resistance mutations.
- Functional genomics for identifying drug target and synergistic drug combination: Our CRISPR-based pooled and high-content screening platform, together with AI-based perturbation prediction models, enables the high-throughput identification of novel drug targets and candidate drug combinations in various disease areas.
- Validated through Biochemical Assays: All in silico predictions will be validated through first in vitro and then in vivo experiments carried out with collaborators in broader IHC network.
- Translational Research: Collaborations will also be established with industry partners to translate our findings towards helping patients and communities.
- Electronic Health Record + Multiomics Continuity: We will pioneer the seamless integration of Electronic Health Records and Multiomics Continuity data, providing a comprehensive understanding of patients’ health profiles to drive forward drug and device development for precision medicine in the face of global health challenges.
Director: Pratyush Tiwary, PhD
Faculty:
PhD students:
- Suemin Lee
- Lukas Herron
Postdocs:
- Xinyu Gu
- Da Teng
- Yunrui Qiu
- Vipin Menon
- Yang Su
Project highlights:
- Wei Li, PhD, Appointed Associate Professor of Pharmacology and Physiology at UMSOM (UMSOM, July 2025)
- UMD’s Pratyush Tiwary Receives Early Career Award from American Chemical Society (Mar 2025)
- Artificial Intelligence Speeding Discovery of New Drugs (Aug 2024)
Recent publications:
- af2rave: Protein Ensemble Generation with Physics-based Sampling
- Hierarchical AF2RAVE for Multiconformation Virtual Screening Targeting S100 Ca2+-Binding Proteins
- Generative AI for computational chemistry: A roadmap to predicting emergent phenomena
- Inferring phase transitions and critical exponents from limited observations with thermodynamic maps
- Thermodynamics-inspired explanations of artificial intelligence
- Calculating Protein–Ligand Residence Times through State Predictive Information Bottleneck Based Enhanced Sampling
