Cheminformatics
Internal training, University of Medicine and Pharmacy at Ho Chi Minh city, Department of Organic Chemistry, 2022
This is a set of eight (8) tutorials on basic information of cheminformatics using the Google Colab free cloud-computing environment in Summer 2022.
Introduction
These tutorials were created between Jan-June 2022 as part of the MedAI Training Session for full execution over Google Colab and remote accesibility via web browsers.
Each tutorial includes a brief introduction of the activities to be performed, installation instructions of the open-source software to be used in each session and several programming, visualization and data analysis activities to be achieved during the tutorial.
After this training session, you will know:
- Package using in cheminformatics: RDKit
- SMILES and SMART structure
- Visualize molecules and database
- ADMET and virtual screening filtrations
- Molecular descriptors and fingerprints
- Molecular Similarity
- Clustering techniques
Description of the Tutorials
The following is a brief description of each tutorial, along with the open-source software used for each task:
| Tutorial | Description | Software | |
|---|---|---|---|
Lab.01  | Warm-up on Colab and Brief Review of RDKit functions | RDKit, mols2grid, tqdm, mols2grid, sklearn, pingouin | |
| Lab.02 | Understanding SMILES structure and different types of data files | RDKit, mols2grid, tqdm | |
| Lab.03 | Understanding SMART structure | RDKit, mols2grid, tqdm | |
| Lab.04 | Visualization techniques for molecules and database | RDKit, mols2grid, tqdm, sklearn, umap-learn | |
| Lab.05 , Homework | Drug-likeliness filtration (ADMET, PAINS,…) | RDKit, mols2grid, tqdm, seaborn, sklearn, pingouin, PyTDC | |
| Lab.06 | Definition of molecular descriptors and fingerprints | RDKit, mols2grid, tqdm, mordred, padelpy, CATS2D, map4, tmap, mhfp, mol2vec | |
| Lab.07 | Molecular Similarity with RDKit | RDKit, mols2grid, tqdm, | |
| Lab.08 | Molecular Clustering with Butina algorithm | RDKit, mols2grid, tqdm |