Developing Behavioral Interventions and Visual Intervention Displays for a Mobile Health Application

This MQP focused on developing user interfaces that are effective in visualizing data in mobile health apps, developing novel ways to deliver data driven interventions and discovering patterns from mobile health use data through the use of association rules

Promoting Research through Improved Online Collaboration Software

The purpose of this project was to create an online platform to facilitate and promote research collaboration among faculty and students at the Financial University in Moscow. We gathered data on this problem through holding interviews and focus groups. Using this data we decided which online research collaboration tool would work best for the university, and we developed a prototype SharePoint website that delivers the features we determined to be most essential for successful online research collaboration

Macrocyclization of linear molecules by deep learning to facilitate macrocyclic drug candidates discovery

Abstract Interest in macrocycles as potential therapeutic agents has increased rapidly. Macrocyclization of bioactive acyclic molecules provides a potential avenue to yield novel chemical scaffolds, which can contribute to the improvement of the biological activity and physicochemical properties of these molecules. In this study, we propose a computational macrocyclization method based on Transformer architecture (which we name Macformer). Leveraging deep learning, Macformer explores the vast chemical space of macrocyclic analogues of a given acyclic molecule by adding diverse linkers compatible with the acyclic molecule. Macformer can efficiently learn the implicit relationships between acyclic and macrocyclic structures represented as SMILES strings and generate plenty of macrocycles with chemical diversity and structural novelty. In data augmentation scenarios using both internal ChEMBL and external ZINC test datasets, Macformer display excellent performance and generalisability. We showcase the utility of Macformer when combined with molecular docking simulations and wet lab based experimental validation, by applying it to the prospective design of macrocyclic JAK2 inhibitors