Principal Investigator — Division of Bioinformatics and Biostatistics

Zhichao Liu, Ph.D.
(870) 543-7121
NCTRResearch@fda.hhs.gov  

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About  |  Publications  |  Lab Member

Background

Dr. Zhichao Liu is the technical leader for the Artificial Intelligence Research Force (AIRForce) in the Division of Bioinformatics & Biostatistics at FDA’s National Center for Toxicological Research (NCTR). Dr. Liu's background spans the fields of chemistry, biology, and computer science and he has led multiple cutting-edge projects in designing, implementing, and deploying artificial intelligence (AI)/machine learning solutions for advanced regulatory sciences. Specifically, Dr. Liu developed the standard pipeline with AI-powered drug repositioning to help the industry seek the optimal route to accelerate the drug-development efficacy from an advanced regulatory-sciences perspective. Furthermore, Dr. Liu leveraged the AI/machine learning solutions for promoting predictive toxicology with successful models adopted by the industry and regulatory process. His accomplishments are reflected by six FDA-wide awards, nine NCTR-level awards, two scientific community-level awards, and more than 100 peer-reviewed publications:

• 2021 FDA Outstanding Inter-Center Scientific Collaboration (Group) for “AI-Powered Drug Repurposing for Combating COVID-19.”
• 2020 NCTR Director's Publication Award for Data Methods/Analysis/Study Design for generating the largest drug list classified for Drug Induced Liver Injury Severity and Toxicity (DILIst).
• 2020 NCTR Director’s Publication Award for a publication addressing the critical steps of applying next-generation sequencing for rare disease diagnoses, which impacts over 350 million people worldwide.
• 2020 SOT Computational Toxicology SS Pfizer Paper of the Year Award for the publication titled "Integrating Adverse Outcome Pathways (AOPs) and High Throughput In Vitro Assays for Better Risk Evaluations, a Study with Drug-Induced Liver Injury (DILI).”
• 2019 NCTR Director’s Publication Award for Basic, Translational or Applied Science for a publication on the lessons learned from 20 years anticancer drug development
• 2019 NCTR Special Act Award for outstanding contributions to the Division of Bioinformatics and Biostatistics for AI development.
• 2017 FDA Chief Scientist Publication Award for Basic, Translational and Applied Science for a publication describing a novel concept reusing oncology drugs for rare diseases by examining similar mutations and pathways common to cancers and rare diseases.
• 2016 FDA Outstanding Junior Investigator for consistently important contributions to collaborative research involving drug repositioning for rare diseases, DILI and liver carcinogen predictors, and regulatory text and data mining.
• 2016 FDA Commissioner’s Special Citation Research to the Review and Return (R2R) Team for a Cross Center Bioinformatics Projects Benefiting Regulatory Business Processes.
• 2016 FDA Outstanding Inter-Center Scientific Collaboration for the work with FDALabel. The FDA Label Team developed a bioinformatics tool and relational database for FDA drug labeling to aid regulatory decisions making and drug review in advancing translational and regulatory sciences.
   

Research Interests

Dr. Liu’s research interests lie in applying AI and deep learning for promoting precision medicine. This includes developing innovative approaches for AI-powered drug repositioning for rare diseases and emerging infections, developing AI-driven strategies and frameworks to facilitate text-mining performance for diverse document types and infrastructures, and developing AI-based flexible and integrative risk-prediction systems for drug-safety evaluation. Dr. Liu is also interested in developing and applying standard pipelines for genomic data (e.g., microarray/next-generation sequencing [NGS]) analysis for drug-safety and -efficacy questions along with designing and developing databases and visualization systems that allow interactive exploration and complex interior relationships embedded in biological-data profiles.

Professional Societies/National and International Groups

The American Association of Pharmaceutical Scientists
Member
2020 – Present

American Chemical Society
Member
2012 – Present

Arkansas Bioinformatics Consortium
Member
2012 – Present

International Society for Computational Biology
Member
2012 – Present

MidSouth Computational Biology and Bioinformatics Society
Member
2012 – Present

Society of Toxicology
Member
2015 – Present

Select Publications

InferBERT: A Transformer-Based Causal Inference Framework for Enhancing Pharmacovigilance.
Wang X., Xu X., Tong W., Roberts R., and Liu Z.
Frontiers in Artificial Intelligence. 2021, 4: 659622.

DICE: A Drug Indication Classification and Encyclopedia for AI-based Indication Extraction.
Bhatt A., Roberts R., Chen X., Li T., Connor S., Hatim Q., Mikailov M., Tong W., and Liu Z.
Frontiers in Artificial Intelligence. 2021, https://doi.org/10.3389/frai.2021.711467.

AI-Based Language Models Powering Drug Discovery and Development.
Liu Z., Roberts R.A., Lal-Nag M., Chen X., Huang R., and Tong W.
Drug Discovery Today. 2021, https://doi.org/10.1016/j.drudis.2021.06.009.

X-CNV: Genome-Wide Prediction of the Pathogenicity of Copy Number Variations.
Zhang L., Shi J., Ouyang J., Zhang R., Tao Y., Yuan D., Lv C., Wang R., Ning B., Roberts R., Tong W., Liu Z., and Shi T.
Genome Medicine. 2021, (13): 132.

Unraveling Gene Fusions for Drug Repositioning in High-risk Neuroblastoma.
Liu Z., Chen X., Roberts R., Huang R., Mikailov M., and Tong W.
Frontiers in Pharmacology. 2021, 768.

Toxicogenomics: A 2020 Vision.
Liu Z., Huang R., Roberts R., and Tong W.
Trends in Pharmacological Sciences. 2019, 40(2), 92-103.

DeepDILI: Deep Learning-Powered Drug-Induced Liver Injury Prediction Using Model-Level Representation.
Li T., Tong W., Roberts R., Liu Z., and Thakkar S.
Chemical Research in Toxicology. 2021, 34 (2), 550-565.

Drug-Induced Rhabdomyolysis Atlas (DIRA) for Idiosyncratic Adverse Drug Reaction Management.
Wen Z., Liang Y., Hao Y., Delavan B., Huang R., Mikailov M., Tong W., Li M., and Liu Z*.
Drug Discovery Today. 2019, 24 (1), 9-15.

Computational Drug Repositioning for Rare Diseases in the Era of Precision Medicine.
Delavan B., Roberts R., Tong W., and Liu Z.
Drug Discovery Today. 2018, 23 (2), 382-394.

Lessons Learned from Two Decades of Anticancer Drugs.
Liu Z., Delavan B., Roberts R., and Tong W.
Trend in Pharmacological Sciences. 2017, 38 (10), 852-872.

In Vitro to In Vivo Extrapolation (IVIVE) for Drug-Induced Liver Injury Using a Pair Ranking (PRank) Method.
Liu Z.*, Fang H., Borlak J., Roberts R., and Tong W.
ALTEX. 2017, doi.org/10.14573/altex.1610311.
 
Potential Reuse of Oncologic Drugs for the Treatment of Rare Diseases.
Liu Z.*, Fang H., Slikker W. Jr., and Tong W.
Trend in Pharmacological Sciences. 2016, 37, 843-857. 

Deciphering miRNA Transcription Factor Feed-Forward Loops to Identify Drug Repurposing Candidates for Cystic Fibrosis. 
Liu Z., Borlak J., and Tong W.
Genome Medicine. 2014, 6:94.
  
In Silico Drug Repositioning: What We Need to Know.
Liu Z., Fang H., Reagan K., Xu X., Mendrick D., Slikker W. Jr., and Tong W.
Drug Discovery Today. 2013, 18(3–4): 110–115.
  
Translating Clinical Findings into Knowledge in Drug Safety Evaluation - Drug-Induced Liver Injury Prediction System (DILIps).
Liu Z., Shi Q., Kelly R., Ding D., Fang H., and Tong W.
PLoS Computational Biology. 2011, 7(12): e1002310.

Lab Member

Contact information for all lab members:
(870) 543-7121
NCTRResearch@fda.hhs.gov

Arjun Bhatt
Contractor

Xi Chen
Contractor

Brian Delavan
Contractor

Ting Li
Contractor

Bushra Sajid
Contractor

Liyuan Zhu
Contractor