Streamlit (Web Application in Python) - Bioinformatics Project from Scratch - Drug Discovery #6 (Deploy Model as Web App) | Streamlit #22

This is Part 6 in a multi-part video series on Bioinformatics Project from scratch. In this video, I will show you how to deploy the machine learning model as a web app. Essentially, this web app will serve as a Bioinformatics tool that will allow users the ability to predict whether a compound of interest has favorable biological activity against the target protein or not. - 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. - Recap of Part 2, I have shown you how to calculate Lipinski descriptors (molecular descriptors proposed by Christopher Lipinski for predicting their likelihood of being drug-like molecules) and performing Exploratory Data Analysis on these Lipinski descriptors. Particularly, the EDA are based on making simple box plots and scatter plots to discern differences of the active and inactive sets of compounds. - Recap of Part 3, I have made some changes to the target protein to be Acetylcholinesterase as it provides a larger dataset to work with. We have already computed the molecular descriptors using the PADEL-Descriptor software and prepare the dataset (X and Y dataframes) that will be used in this video for Model Building. - Recap of Part 4, I have show you how to use the computed molecular descriptors from Part 3 (as the X variables) to build a regression model for predicting the pIC50 values (the Y variable). - Recap of Part 5 in a multi-part video series on Bioinformatics Project from scratch. In this video, I will show you how to quickly build and compare several regression models (quantitative structure-activity relationship or QSAR) of the Acetylcholinesterase inhibitors using the lazypredict library in Python. ⭕ Jupyter notebook (Acetylcholinesterase inhibitors) Part 1 https://github.com/dataprofessor/code/blob/master/python/CDD_ML_Part_1_Acetylcholinesterase_Bioactivity_Data_Concised.ipynb Part 2 https://github.com/dataprofessor/code/blob/master/python/CDD_ML_Part_2_Acetylcholinesterase_Exploratory_Data_Analysis.ipynb Part 3 https://github.com/dataprofessor/code/blob/master/python/CDD_ML_Part_3_Acetylcholinesterase_Descriptor_Dataset_Preparation.ipynb Part 4 https://github.com/dataprofessor/code/blob/master/python/CDD_ML_Part_4_Acetylcholinesterase_Regression_Random_Forest.ipynb Part 5 https://github.com/dataprofessor/code/blob/master/python/CDD_ML_Part_5_Acetylcholinesterase_Compare_Regressors.ipynb Part 6 https://github.com/dataprofessor/bioactivity-prediction-app Paper https://peerj.com/articles/2322/ --------------- 🌟 Subscribe to this YouTube channel https://www.youtube.com/dataprofessor?sub_confirmation=1 🌟 Join the Newsletter of Data Professor http://newsletter.dataprofessor.org 🌟 Buy me a coffee https://www.buymeacoffee.com/dataprofessor 🌟 Download Kite for FREE https://www.kite.com/get-kite/?utm_medium=referral&utm_source=youtube&utm_campaign=dataprofessor&utm_content=description-only ⭕ 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/dataprofessor-pandas ✅ Python Data Science Project: https://bit.ly/dataprofessor-python-ds ✅ R Data Science Project: https://bit.ly/dataprofessor-r-ds ⭕ Follow us: ✅ Medium: http://bit.ly/chanin-medium ✅ FaceBook: http://facebook.com/dataprofessor/ ✅ Website: http://dataprofessor.org/ (Under construction) ✅ Twitter: https://twitter.com/thedataprof/ ✅ Instagram: https://www.instagram.com/data.professor/ ✅ LinkedIn: https://www.linkedin.com/in/chanin-nantasenamat/ ✅ GitHub 1: https://github.com/dataprofessor/ ✅ GitHub 2: https://github.com/chaninlab/ #dataprofessor #bioinformatics #drugdiscovery #drugdesign #cheminformatics #bioinformaticproject #drug #drugs #molecule #molecules #machinelearning #dataprofessor #bigdata #QSAR #QSPR #machinelearning #pythonprogramming #datascience #datamining #bigdata #datasciencetutorial #ai #artificialintelligence