Machine learning models for drug discovery based on activity on human alpha-Glucosidase

Machine learning models which are trained on PubChem datasets AID 2100 and AID 2242 to obtain drug candidates (activators and inhibitors of alpha glucosidase) for Type II diabetes and Pompe's disease. AID 2100 and AID 2242 are PubChem datasets for inhibitors and activators of Human Alpha-glucosidase enzyme respectively.

Mold2 was used to compute 777 molecular descriptors of the compounds obtained from PubChem. These molecular descriptors were used to train different ML algorithms to predict whether a given compound is an activator/inhibitor of Alpha-glucosidase.

Steps to run

• Download the PubChem datasets for actives and inactive compounds for AID 2100 and AID 2242
• Compute the molecular descriptors of the compounds with Mold2
• Create virtual environment by running either python -m venv <name-of-virtual-environment> or virtualenv <name-of-virtual-environment>. Virtualenv can be installed with pip.
• Activate the virtual environment. on Windows, run the following command in Powershell in the root directory. .\<name-of-virtual-environment>\Scripts\activate
• After the virtual environment is activated, install all the dependencies for this projects by running pip install -r requirements.txt in the root directory
• Run jupyter notebook in the root directory. If you don't already have Jupyter notebooks, you can install by running pip install jupyterlab

Understanding the structure of the models

Due to the data imbalance between the active and inactive compounds, synthetic minority oversampling technique (SMOTE) was used.

Each algorithm is trained with 3 different approaches for the activators and those same 3 approaches for the inhibitors. ie. 6 models for each algorithm

• The first approach uses ALL training examples from the inactive compounds and performs SMOTE on the active compounds. Format for naming: <activators|inhibitors_over.ipynb>
• The second approach randomly samples part of the inactive compounds and performs SMOTE on the active compounds. This approach is similar to the first except it has fewer synthetic training examples for the active compounds. Format for naming: <activators|inhibitors_mid.ipynb>
• The third approach randomly samples just the exact amount of inactive compounds as the active compounds to achieve balance. Format for naming: <activators|inhibitors_under.ipynb>