Wendy Cornell  Wendy Cornell photo         

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Global Strategy Lead and YKT Team Manager, Molecular Drug Discovery Technologies, PRSM, Healthcare and Life Science Research
Thomas J. Watson Research Center, Yorktown Heights, NY USA


Professional Associations

Professional Associations:  American Chemical Society

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More information:  LinkedIn profile  |  Google Scholar profile


IBM Roles

Wendy's primary role is IBM Research Strategy Lead for Molecular Drug Discovery Technologies where she leverages her broad experience base in pharmaceutical industry use cases and existing solutions to guide efforts across the IBM Research Labs to develop innovative drug discovery capabilities. At the local level, Wendy serves as Manager of the Yorktown Heights-based Drug Discovery Technologies team, leading a group of simulation and AI experts in the development of next-generation tools to support small molecule and biologicdesign by exploiting protein structure and dynamic information. Wendy is credentialed as an IBM Q Ambassador for quantum computing and previously served as Life Science Research liaison to Watson Health.

Wendy joined IBM in 2016 after twenty years in the pharmaceutical industry where she led teams at Merck and Novartis in the development and application of chemistry modeling, cheminformatics, machine learning, text mining, and knowledge management solutions to support science-based pipeline decisions for lead finding and optimization, target assessment, preclinical safety, and preclinical competitive intelligence.

Pharmaceutical Industry Experience

During her time in the pharmaceutical industry, Wendy recruited and led science and solutions groups located in the Research, Licensing, and IT organizations:

As Director of Knowledge Discovery and Management at Merck (2011-2015) her competitive intelligence (CI) team developed NLP-based text mining solutions to support preclinical CI and Licensing using content from Medline abstracts, ClinicalTrials.gov, NIH Reporter, patents, conference abstracts, other external sources, and proprietary content. Teams supporting proprietary document management extracted conclusions from internal preclinical safety assessment study reports to correlate short term and long term results and identify capability gaps and also protected, integrated, and exposed at-risk legacy paper files.

As Director of Chemistry Modeling and Informatics at Merck (2004-2010), her group supported drug discovery projects in the four therapeutic areas present at Rahway, Merck’s then largest research site, using similarity methods, QSAR, docking, molecular dynamics, and other approaches to identify new leads, maximize on-target potency, and minimize off-target effects.  She managed teams that developed novel solutions to assess target bindability and kinase selectability by integrating 3D protein structure, protein sequence, and activity data.  

At Merck Wendy also served on the New Technologies Review and Licensing Committee as chair of the Chemistry working group and member of the IT working group, identifying internal capability gaps and evaluating and integrating external solutions.  Her group initiated the first major collaboration between Modeling and Process Chemistry to establish vibrational CD capabilities which led to an ongoing and greatly expanded engagement.

 Professional Service

A Fellow of the American Chemical Society (ACS), Dr. Cornell has held multiple leadership roles in ACS governance, most recently serving as Chair of the Committee on Chemical Abstracts Service (CCAS), connecting ACS members with CAS, an ACS division which provides chemical information and has $1B in assets and $500M in annual revenues. She is past Program Chair and past Chair of the Computers in Chemistry (COMP) technical division and currently serves as COMP Leadership Development Chair. She is also a member of the SAB for the Stony Brook University Institute of Chemical Biology and Drug Discovery (ICB&DD). A former adjunct faculty member at Robert Wood Johnson Medical School, Dr. Cornell served as primary thesis advisor for a Ph.D. student. She has also served on multiple NIH Small Business Innovation Research (SBIR) panels.


Wendy received her Ph.D. from the University of California at San Francisco (UCSF) working with the late Peter Kollman and resulting in a primary publication describing the AMBER classical force field which has received > 14,000 citations.  After UCSF she did a brief postdoctoral stint at European Molecular Biology Laboratory (EMBL) in Heidelberg. Wendy received an MBA from Case Western Reserve University Weatherhead School of Management in Cleveland, OH on the Cleveland Clinic Healthcare track and enjoys studying the business impact of information technologies.

Key Publications
Community Methods Assessment
Suzanne Ackloo, Suzanne Ackloo, Rima Al-awar, Rima Al-awar, Rommie E. Amaro, Cheryl H. Arrowsmith, Cheryl H. Arrowsmith, Hatylas Azevedo, Robert A. Batey, Yoshua Bengio, Ulrich A.K. Betz, Cristian G. Bologa, John D. Chodera, Wendy D. Cornell, Ian Dunham, Gerhard F. Ecker, Kristina Edfeldt, Aled M. Edwards, Aled M. Edwards, Michael K. Gilson, Claudia R. Gordijo, Claudia R. Gordijo, Gerhard Hessler, Alexander Hillisch, Anders Hogner, John J. Irwin, Johanna M. Jansen, Daniel Kuhn, Andrew R. Leach, Alpha A. Lee, Uta Lessel, John Moult, Ingo Muegge, Tudor I. Oprea, Benjamin G. Perry, Patrick Riley, Kumar Singh Saikatendu, Vijayaratnam Santhakumar, Vijayaratnam Santhakumar, Matthieu Schapira, Matthieu Schapira, Cora Scholten, Matthew H. Todd, Masoud Vedadi, Masoud Vedadi, Andrea Volkamer, Timothy M. Willson
Nat Rev Chem in press
Generative Molecule Creation and Activity Classification / Deep Learning
Jannis Born, Tien Huynh, Astrid Stroobants, Wendy D. Cornell, and Matteo Manica
J Chem Info Model online ahead of print
Seung-gu Kang, Joseph A. Morrone, Jeffrey K. Weber, and Wendy D. Cornell
arxiv 2021
J Chem Info Model  in review 
QSAR Interpretation / Deep Learning
Jeffrey K. Weber, Joseph A. Morrone, Sugato Bagchi, Jan D. Estrada Pabon, Seung-gu Kang, Leili Zhang & Wendy D. Cornell 
J Comput-Aided Mol Des 2021

3D Docking Pose Prediction / Deep Learning
Joseph A. Morrone, Jeffrey K. Weber, Tien Huynh, Heng Luo, and Wendy D. Cornell
J Chem Info Model 2020 
Text Mining / NLP
Role of chronic toxicology studies in revealing new toxicities 
Galijatovic-Idrizbegovic, Alema and Miller, Judith E and Cornell, Wendy D and Butler, James A and Wollenberg, Gordon K and Sistare, Frank D and DeGeorge, Joseph J 
Regulatory Toxicology and Pharmacology 82, 94-98, Elsevier, 2016
Application of an automated natural language processing (NLP) workflow to enable federated search of external biomedical content in drug discovery and development 
McEntire, Robin and Szalkowski, Debbie and Butler, James and Kuo, Michelle S and Chang, Meiping and Chang, Man and Freeman, Darren and McQuay, Sarah and Patel, Jagruti and McGlashen, Michael and others 
Drug Discovery Today 21(5), 826--835, Elsevier, 2016
Developing timely insights into comparative effectiveness research with a text-mining pipeline 
Chang, Meiping and Chang, Man and Reed, Jane Z and Milward, David and Xu, Jinghai James and Cornell, Wendy D 
Drug discovery today 21(3), 473--480, Elsevier, 2016 
QSAR / Machine Learning 
QSAR Prediction of Passive Permeability in the LLC-PK1 Cell Line: Trends in Molecular Properties and Cross-Prediction of Caco-2 PermeabilitiesSherer, Edward C and Verras, Andreas and Madeira, Maria and Hagmann, William K and Sheridan, Robert P and Roberts, Drew and Bleasby, Kelly and Cornell, Wendy D
Molecular Informatics 31(3-4), 231--245, Wiley Online Library, 2012
Drug-like density: a method of quantifying the “bindability” of a protein target based on a very large set of pockets and drug-like ligands from the Protein Data Bank 
Sheridan, Robert P and Maiorov, Vladimir N and Holloway, M Katharine and Cornell, Wendy D and Gao, Ying-Duo 
Journal of chemical information and modeling 50(11), 2029--2040, ACS Publications, 2010
QSAR models for predicting the similarity in binding profiles for pairs of protein kinases and the variation of models between experimental data sets 
Sheridan, Robert P and Nam, Kiyean and Maiorov, Vladimir N and McMasters, Daniel R and Cornell, Wendy D 
Journal of chemical information and modeling 49(8), 1974--1985, ACS Publications, 2009
Similarity Searching and Docking
Comparison of topological, shape, and docking methods in virtual screening 
McGaughey, Georgia B and Sheridan, Robert P and Bayly, Christopher I and Culberson, J Chris and Kreatsoulas, Constantine and Lindsley, Stacey and Maiorov, Vladimir and Truchon, Jean-Francois and Cornell, Wendy D 
Journal of chemical information and modeling 47(4), 1504--1519, ACS Publications, 2007
Recent evaluations of high throughput docking methods for pharmaceutical lead finding--consensus and caveats 
Cornell, Wendy D 
Annual Reports in Computational Chemistry2, 297--323, Elsevier, 2006
Classical Force Fields (14,000+ citations)
A second generation force field for the simulation of proteins, nucleic acids, and organic molecules 
Cornell, Wendy D and Cieplak, Piotr and Bayly, Christopher I and Gould, Ian R and Merz, Kenneth M and Ferguson, David M and Spellmeyer, David C and Fox, Thomas and Caldwell, James W and Kollman, Peter A 
J Am Chem Soc 117(19), 5179--5197, ACS Publications, 1995