Andrew Su, PhD
Professor
Department of Integrative Structural and Computational Biology
Elden and Verna Strahm Chair for Medical Research
Research Focus
Our lab is interested in biomedical discovery using quantitative methods. We apply the tools of computer science, statistics, and increasingly artificial intelligence to a range of problems spanning drug discovery, biomedical knowledge representation, gene and variant annotation, and single-cell biology. Across all of our work, we are committed to the principles of open source, open data, and open science.
Our current research activities generally fall into three groups.
First, we build computational tools and infrastructure to organize and connect biomedical knowledge at scale. We develop and maintain widely used resources, including the BioThings suite (MyGene.info, MyVariant.info, MyChem.info) of biomedical APIs that collectively serve over 30 million requests per month. More recently this work has grown into large-scale biomedical knowledge graphs, including our contributions to the NCATS Biomedical Data Translator and the NIAID Data Ecosystem. These projects integrate data across hundreds of sources so they can be queried and reasoned over computationally.
Second, we directly engage in biomedical discovery by mining and integrating these large, heterogeneous data sets. We use knowledge graphs and statistical methods to generate and prioritize mechanistic hypotheses. For example, we are developing tools and approaches for identifying drug-repurposing opportunities and mapping the mechanisms by which drugs act (as in our DrugMechDB resource). Our emphasis is on translating massive data sets into a small number of testable, evidence-supported predictions.
Third, we actively seek out new collaborations with experimental scientists to explore novel data sets and technology platforms. The rate of biological data generation continues to accelerate, and bioinformatics plays a critical role in the warehousing, analysis, and visualization of these data. We collaborate with research groups both locally and globally, spanning areas such as single-cell transcriptomics, circadian biology, and infectious disease. The focus of this work is on efficiently translating large data sets into testable hypotheses.
Fourth and most recently, we are actively exploring the use of Agentic AI tools for science. A growing thread of our work explores how AI agents and large language models can accelerate biomedical research -- augmenting how scientists generate ideas, synthesize the literature, and collaborate. We are developing and evaluating agentic tools and platforms that bring these capabilities to working researchers, with an emphasis on transparency, reliability, and open infrastructure.
Select Publications
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Gonzalez-Cavazos, Adriana Carolin C.; Tu, Roger; Sinha, Meghamala; Su, Andrew I. A case-based explainable graph neural network framework for mechanistic drug repositioning. 2026, 42.
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Joy, Janet; Su, Andrew I. Federated knowledge retrieval elevates large language model performance on biomedical benchmarks. 2026, 15.
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Lee, Kwang I.; Gamini, Ramya; Olmer, Merissa; Ikuta, Yasunari; Hasei, Joe; Baek, Jihye; Alvarez-garcia, Oscar; Grogan, Shawn P.; D'lima, Darryl D.; Asahara, Hiroshi; Su, Andrew I.; Lotz, Martin K. Mohawk is a transcription factor that promotes meniscus cell phenotype and tissue repair and reduces osteoarthritis severity.. Science Translational Medicine 2020, 12.
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Albrecht, Randy A.; Benner, Christopher; Burgstaller-muehlbacher, Sebastian; Cao, Jianli; Chan, Jasper F.; Chanda, Sumit K.; Chang, Max W.; Chapman, Mackenzie E.; Chatterjee, Arnab K.; Cheng, Kuoyuan; Choi, Angela; Dejosez, Marion; Garcia-sastre, Adolfo; Glynne, Richard J.; Herbert, Kristina M.; Hull, Mitchell V.; Jesus, Paul D.; Johnson, Jeffrey R.; Lendy, Emma K.; Liu, Wen-chun; Martin-sancho, Laura; Martinez-sobrido, Luis; Matsunaga, Naoko; Mesecar, Andrew D.; Miorin, Lisa; Nguyen, Courtney; Nguyen, Tu-trinh H.; Pache, Lars; Poon, Vincent K.; Pu, Yuan; Rathnasinghe, Raveen; Riva, Laura; Rubanov, Andrey; Ruppin, Eytan; Schotsaert, Michael; Schultz, Peter G.; Sit, Ko-yung Y.; Su, Andrew I.; Sun, Ren; Teriete, Peter; White, Kris M.; Yin, Xin; Yuan, Shuofeng; Yuen, Kwok-yung; Zwaka, Thomas P. Discovery of SARS-CoV-2 antiviral drugs through large-scale compound repurposing.. Nature 2020, 113-119.
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Callaghan, Jackson; Xu, Colleen H.; Xin, Jiwen; Cano, Marco Alvarad A.; Riutta, Anders; Zhou, Eric; Juneja, Rohan; Yao, Yao; Narayan, Madhumita; Hanspers, Kristina; Agrawal, Ayushi; Pico, Alexander R.; Wu, Chunlei; Su, Andrew I. BioThings Explorer: a query engine for a federated knowledge graph of biomedical APIs. 2023, 39.
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Tsueng, Ginger; Bullen, Emily; Czech, Candice; Welzel, Dylan; Collares, Leandro; Lin, Jason; Rodolpho, Everaldo; Qazi, Zubair; Acosta, Nichollette; Mayer, Lisa M.; Venkatachari, Sudha; Mitrović Vučičević, Zorana; Burman, Poromendro N.; Jain, Deepti; DiGiovanna, Jack; Giovanni, Maria; Lin, Asiyah; Van Panhuis, Wilbert; Hughes, Laura D.; Su, Andrew I.; Wu, Chunlei The NIAID Discovery Portal: a unified search engine for infectious and immune-mediated disease datasets. 2026, 11, e0127025.
