The project aims to facilitate the research and development of new drugs by exploring deep learning methods to process molecules and to generate new molecules. The deep learning models that will be experimented include few shot learning, generative adversarial network, and variational autoencoder. We would like to improve these methods specifically for pharmacological datasets, which […]

Most of the drugs used to treat cancer have been originally identified from natural sources. While Nature did a great job selecting those compounds, some of them have shown limitations in the treatment of cancer and others have shown to be insufficient on some cancer types. Furthermore, it exists a gigantic number (nearly infinite) of […]

Using simplified language understandable to a layperson; provide a general, one-paragraph description of the proposed research project to be undertaken by the intern(s) as well as the expected benefit to the partner organization. {100 – 150 words) The project aims to develop a molecular compounds database to accelerate drug discovery. Compounds shared by chemical providers […]

Better understanding Drug-Drug interactions (DDIs) is crucial for planning therapies and drugs co-administration. While, considerable efforts are spent in labor-intensive in vivo experiments and time-consuming clinical trials, understanding the pharmacological implications and adverse side-effects for some drug combinations is challenging. The majority of interactions remains undetected until therapies are prescribed to patients. We propose to […]

The project aims to facilitate the research and development of new drugs by exploring Machine Learning methodology useful for both the generation of new molecules and the prediction of molecule properties. Doing so will involve training deep learning models on a large number of small, heterogeneous datasets, with the objective of transferring learned representations quickly […]