How to apply
|Who to email@example.com|
|Email subject||Start with "[ML Master]"|
|What to include?||
If you apply with your own project idea
Graph Neural Networks (GNN) are uniquely suited to represent functional compound classes in ligand based virtual screening (https://doi.org/10.1186/s13321-020-00479-8). We will setup and train a GNN on known inotrope compounds and screen i) the drug repurposing hub and ii) an in-house library for novel drug candidates that will then be phenotypically screened on our EHM based inotrope screening platform.
In a recent seminal paper (https://doi.org/10.1016/j.cell.2020.01.021) Stokes and Collogues have outlined a generic approach on how to device and a generic graph neural network on a subset of chemical compounds with a given biological activity and screen large libraries of drug-like compounds for candidates with similar, in their case bactericidal, activity.
The student will setup chemical a fingerprints database using extended connectivity fingerprints (ECFPs) and SMILES representations from existing drugs generated using OpenChem, a PyTorch based toolkit for computational chemistry. Drugs will be classified into cardioactive and cardio-innactive compounds and machine learning methods of different complxity (RNN, GNN) will be compared.
Chemistry department will provide a virtual library of undocumented drug-like compounds accessible by their novel synthesis methods. The trained NN is used to identify promising candidates that will be synthesised and tested for inotropy and chronotropy.
We offer hands-on master project which combines deep learning and computational chemistry and merges the unique on-site expertise of CIDAS, chemistry, and pharmacology. The candidate should have experience in data science and python, basic chemistry would be an asset.