Next-generation molecular docking leveraging artificial intelligence techniques to understand large-scale ligand binding data sets

Principal Investigator: Rafaël Najmanovich
Theme : Health
Competition : 2017 Bioinformatics and Computational Biology (B/CB) Competition
Status : In progress
Start : Oct. 1, 2018
End: Sept. 30, 2021
Budget : $500,000.00

Nothing in nature exists in isolation, from the smallest molecule to the largest trees or animals. Everything interacts. Rafael Najmanovich of the Université de Montréal is concerned with the smallest molecule end of things – specifically the interactions between small molecules and the proteins that govern most cellular metabolism and signalling. He wants to understand the “molecular recognition” that happens between these small molecules and proteins to better understand the biological processes in order to develop new drugs.


Ligands (in general small-molecules) bind to proteins within surface cavities. The prediction of these interactions is done through docking simulations. Docking methods are widely used and accepted today but have yet to reach the level of realism, speed and accuracy required to fulfill their full potential in drug design. Najmanovich’s laboratory has already developed FlexAID (Flexible Artificial Intelligence Docking), which outperforms widely used docking methods. Now, they are developing next-generation docking software that will increase the biological accuracy with which researchers can model docking processes, speed up simulations to rank small molecules and focus on specific protein families that are important in drug design.


The docking methods being developed will create open-source software that will help understand, from a structural point of view, drug discovery, and enhance our understanding of molecular recognition. They will also help advance pharmaceutical research to bring about social and economic benefits for Canadians.


Lead Genome Centre: Génome Québec