Bioinformatics Project from Scratch - Drug Discovery Part 2 (Exploratory Data Analysis)

Data Professor · Beginner ·📊 Data Analytics & Business Intelligence ·5y ago
This video represents Part 2 in a multi-part video series on Bioinformatics Project from scratch. In this video, I will be showing you how to take the dataset from Part 1 and use the SMILES notation (representing the unique chemical structure of compounds) to compute molecular descriptors. The descriptors that we will be computing are the Lipinski's descriptors (molecular weight, LogP, number of hydrogen bond donors and number of hydrogen bond acceptors). Finally we will then perform exploratory data analysis by making simple box plots and scatter plots to discern differences of the active and inactive sets of compounds. 🌟 Buy me a coffee: https://www.buymeacoffee.com/dataprofessor Recap of Part 1, I have shown you how to collect original dataset in biology that you can use in your Data Science Project. Particularly, I have demonstrated how to download and pre-process the biological activity data from the ChEMBL database. The dataset is comprised of compounds (molecules) that have been biologically tested for their activity towards target organism/protein of interest. ⭕ Code: ✅ Part 1 (concise) Code: https://github.com/dataprofessor/code/blob/master/python/CDD_ML_Part_1_Bioactivity_Data_Concised.ipynb ✅ Part 2 Code: https://github.com/dataprofessor/code/blob/master/python/CDD_ML_Part_2_Exploratory_Data_Analysis.ipynb ⭕ Playlist: Check out our other videos in the following playlists. ✅ Data Science 101: https://bit.ly/dataprofessor-ds101 ✅ Data Science YouTuber Podcast: https://bit.ly/datascience-youtuber-podcast ✅ Data Science Virtual Internship: https://bit.ly/dataprofessor-internship ✅ Bioinformatics: http://bit.ly/dataprofessor-bioinformatics ✅ Data Science Toolbox: https://bit.ly/dataprofessor-datasciencetoolbox ✅ Streamlit (Web App in Python): https://bit.ly/dataprofessor-streamlit ✅ Shiny (Web App in R): https://bit.ly/dataprofessor-shiny ✅ Google Colab Tips and Tricks: https://bit.ly/dataprofessor-google-colab ✅ Pandas Tips and Tricks: https://bit.ly/da
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