Paul G. Francoeur

About

2nd year Computational Biology Doctoral Candidate at CMU/PITT currently working with Dr. David Ryan Koes. I have a MS in Cell and Molecular Biology from GVSU, and a BS in the Cell and Molecular Biology and Mathematics from GVSU. My research interests lie in the development of Machine Learning algorithms and systems to aid in the drug discovery process and their utilization in the development of novel theraputics.

Contact

paf46@pitt.edu

Suite 3064, Biomedical Science Tower 3 (BST3)
Department of Computational and Systems Biology
School of Medicine, University of Pittsburgh
3501 Fifth Avenue
Pittsburgh, PA 15260

Download my CV
here

Current Research Topics

Training Data Augmentation

Convoultional Nerual Networks are successful at image recognition, which is analagous to protein-ligand recognition, but are fundamentally limited by the amount of data that they can train on. For our purposes, the PDB is the limiting factor on the amount of training data that we can utilize. As such, exploring ways to augment the training data could prove useful in expanding the power of a CNN to effectively distinguish good and bad drug-protein pairs, while also providing a method to sidestep current scoring functions. The project, gnina, can be found here. Note that it currently is not at release 1.0

Profilin

Mutants in Profilin have been implicated in ALS. These mutants are the current target that I am utilizing my labs methods to discover drug interactions with for a more robust knowledge of how the mutants function in the disease state and potential treatments.

Publications

Sunseri, J., King, J.E., Francoeur, P.G. et al. Convolutional neural network scoring and minimization in the D3R 2017 community challenge J Comput Aided Mol Des (2018). https://doi.org/10.1007/s10822-018-0133-y

Presentations

Paul Francoeur, David Ryan Koes Protein-Ligand Binding Affinity Prediction with GNINA 8th Drug Discovery Innovation Programme - Boston (2019). Download the poster here
Paul Francoeur, Matthew Ragoza, Rachel Rosenzweig, Jocelyn Sunseri, David Ryan Koes GNINA: Deep Learning for Molecular Docking ACS National Meeting - Boston (2018). Download the poster here

Lab Members.

PI: David Ryan Koes
Graduate Student: Jocelyn Sunseri
Graduate Student: Jonathan King