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.

Teaching

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.

 

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