Bin Chen, Ph.D.

  • Neuropharmacology Faculty
Assistant Professor
Department of Pediatrics and Human Development
Secchia Center, 7th Floor
15 Michigan St. NE
Grand Rapids, MI 49503 (Primary)
Department of Pharmacology & Toxicology
1355 Bogue St
East Lansing, MI 48824 (Secondary)
Phone: 616-234-2819
Fields of Interest: Chen Lab is dedicated to leveraging Big Data and AI for cancer therapeutic discovery

Educational Background

  • 2000-2004- B.A. Chemistry, Chongqing University, China
  • 2007-2009- M.S. Chemical Informatics, Indiana University, Indiana
  • 2009-2012- Ph.D. Informatics, Indiana University, Indiana
  • 2012-2015- Postdoc Bioinformatics, Stanford University, California
  • 2015-2017- Instructor Pediatrics, University of California, San Francisco, California
  • Assistant Professor Pediatrics, University of California, San Francisco, California
  • 2018-Present- Assistant Professor Pediatrics/Pharmacology and Toxicology, Michigan State University, Michigan

Biography & Current Research

Bin trained as a chemist in college, worked as a software engineer before graduate school, trained as a chem/bioinformatician in graduate school, worked as a computational scientist at Novartis, Pfizer and Merck. The long-term goal of Dr. Bin Chen's lab is to develop computational methods and tools to discover new or better therapeutic candidates for cancers through collaborating with bench scientists and clinicians. Rapidly decreasing costs of molecular measurement technologies not only enable profiling of disease sample molecular features (e.g., transcriptome, proteome, metabolome) at different levels (e.g., tissues, single cells), but also enable measuring of cellular signatures of individual drugs in clinically relevant models. Our lab is interested in leveraging these data and artificial intelligence to connect different components (patients, tissues, in vitro models and in vivo models) in translational research. We currently focus on liver cancer, breast cancer, and pediatric cancers.

Current Projects

1. A systems approach to discover new therapeutics

We employ a systems-based approach that identifies drugs that reverse the molecular state of a disease. In this approach, we assess the effect of a drug on a disease molecular signature rather than one single target. Using this approach, we have successfully identified drug candidates for three cancers: Ewing’s sarcoma (Oncotarget, 2016), liver cancer (Gastroenterology, 2017) and basal cell carcinoma (JCI Insight, 2017) in the past few years. In our recent pan-cancer analysis, we found that the potency to reverse cancer gene expression correlates to drug efficacy (Nature Communications, 2017). We are currently applying this approach in the following areas: 1) discovering drugs to overcome drug resistance, 2) discovering novel oncogene inhibitors, and 3) discovering small molecules regulating cell reprogramming.

2. Personalized Cancer Therapy

Current preclinical and clinical approaches select therapies primarily based on actionable mutations, yet patients may have no actionable mutations or multiple actionable mutations that are hard to prioritize, suggesting the need for additional biomarkers. The recent disappointing result from the SHIVA trial indicates that more predictive biomarkers of drug efficacy are needed to select therapies. In addition to mutations, other molecular data (e.g., gene, protein) have been widely explored as biomarkers in personalized therapy, including gene expression based biomarkers such as Oncotype DX and MammaPrint in breast cancer treatment, however, the biomarkers for many cancer drugs are currently quite limited as it takes many years to run clinical trials to identify and validate a biomarker for a single drug. The recent efforts have enabled the large-scale identification of various types of molecular biomarkers through correlating drug sensitivity with molecular profiles of pre-treatment basal cancer cell lines. We are interested in developing computational methods to match these biomarkers to individual patients to inform therapy in the clinic.

3. Deep Learning in Drug Discovery

Deep learning is revolutionizing many fields including drug discovery. We recently employed autoencoder to select reference normal tissue samples. We are currently exploring different deep learning methods in drug optimization and drug induced-gene expression prediction.

Awards & Achievements

  • 2010 CINF Scholarship for Scientific Excellence. ACS Chemical Information Division
  • 2012 Lucille Wert Scholarship. ACS Chemical Information Division
  • 2014 Jason Morrow Trainee Award ASCPT
  • 2014 Presidential Trainee Award ASCPT
  • 2015 Presidential Poster of Distinction The Liver Meeting
  • 2015 Intel Science Talent Search (Intel STS) Research Teacher
  • 2016 LINCS Meeting Travel Fellowship
  • 2017 BD2K K01 Award
  • 2017 NCATS R21

Committees & Activities

I am the founding member of DahShu, a non-profit organization to promote research and education in data science. In addition to annual symposiums, we organize monthly big data seminars.

Selected Publications

Chen B, Wei W, Ma L, Yang B, Gill RM, Chua MS, Butte AJ, So S. Computational Discovery of Niclosamide Ethanolamine, a Repurposed Drug Candidate That Reduces Growth of Hepatocellular Carcinoma Cells In Vitro and in Mice by Inhibiting Cell Division Cycle 37 Signaling. Gastroenterology. 2017 Jun; 152(8):2022-2036. PMID: 28284560.

Chen B, Ma L, Paik H, Sirota M, Wei W, Chua MS, So S, Butte AJ. Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets. Nat Commun. 2017 Jul 12; 8:16022. PMID: 28699633.

Pessetto ZY, Chen B, Alturkmani H, Hyter S, Flynn CA, Baltezor M, Ma Y, Rosenthal HG, Neville KA, Weir SJ, Butte AJ, Godwin AK. In silico and in vitro drug screening identifies new therapeutic approaches for Ewing sarcoma. Oncotarget. 2017 Jan 17; 8(3):4079-4095. PMID: 27863422.

Chen B, Butte AJ. Leveraging big data to transform target selection and drug discovery. Clin Pharmacol Ther. 2016 Mar; 99(3):285-97. PMID: 26659699; PMCID: PMC4785018.

Chen B, Ding Y, Wild DJ. Assessing drug target association using semantic linked data. PLoS Comput Biol. 2012; 8(7):e1002574. PMID: 22859915; PMCID: PMC3390390.

Book Chapters

Chen B, Wang H, Ding Y, Wild D. Synthesis Lectures on the Semantic Web: Theory and Technology. Semantic Breakthrough in Drug Discovery. 2014.