David Koes

David Koes, Ph.D.
Associate Professor

Ph.D. in Computer Science, Carnegie Mellon University

Phone: (412) 383-5745
E-mail: dkoes@pitt.edu
Website: bits.csb.pitt.edu

Research Summary
The goal of my research is to develop novel computational methods and applications that unlock the potential of computational drug discovery to revolutionize the treatment of disease. I develop new applications of machine learning for computational drug discovery as well as discrete algorithms for accelerating the drug discovery workflow. In addition to developing new computational techniques, I deploy these techniques via easy to use online applications and apply them in prospective drug discovery exercises.



MSCBIO2025 Introduction to Bioinformatics Programming in Python (Fall Semester)  This course is a graduate level introduction to programming in Python in the context of computational biology applications.

MSCBIO2065 Scalable Machine Learning for Big Data Biology (Spring Semester)  This course is a rigorous introduction to the effective application of machine learning to large and complex biomedical data.


Recent Publications
McNutt AT, Francoeur P, Aggarwal R, Masuda T, Meli R, Ragoza M, Sunseri J, Koes DR. GNINA 1.0: molecular docking with deep learning. Journal of Cheminformatics. 2021 Dec;13(1):1-20.
Francoeur PG, Masuda T, Sunseri J, Jia A, Iovanisci RB, Snyder I, Koes DR. Three-dimensional convolutional neural networks and a cross-docked data set for structure-based drug design. Journal of Chemical Information and Modeling. 2020 Aug 31;60(9):4200-15.
Allen A, Gau D, Francoeur P, Sturm J, Wang Y, Martin R, Maranchie J, Duensing A, Kaczorowski A, Duensing S, Wu L. Actin-binding protein
profilin1 promotes aggressiveness of clear-cell renal cell carcinoma cells. Journal of Biological Chemistry. 2020 Nov 13;295(46):15636-49.
Sunseri J, Koes DR. libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications. Journal of chemical information and modeling. 2020 Feb 12;60(3):1079-84.
Seshadri K, Liu P, Koes DR. The 3Dmol. js Learning Environment: A Classroom Response System for 3D Chemical Structures. J. Chem. Educ. 2020, 97, 10, 3872–3876

Publications (Google Scholar)
Publications (NCBI)

Project title Proj Start Date Proj End Date Funding Source
New Methods and Tools for Computational Drug
6/1/2021 5/31/2026 NIGMS
CSD&E: Expanding Efficient Conformer Sampling to
Diverse Charged and Neutral Molecules
7/1/2021 7/1/2024 NSF