Replication Package for article Harnessing Machine Learning Models to Repurpose Drugs Targeting HIV-1 Integrase, Protease, and Reverse Transcriptase

# Replication Package for paper "Harnessing Machine Learning Models to Repurpose Drugs Targeting HIV-1 Integrase, Protease, and Reverse Transcriptase" This package reproduces the experiments described in the paper:Harnessing Machine Learning Models to Repurpose Drugs Targeting HIV-1 Integrase, Protease, and Reverse Transcriptase Ciprian-Bogdan Chirila, University Politehnica Timisoara, 2 V. Parvan Avenue, Timisoara 300223, Romania, [e-mail] Luminita Crisan, "Coriolan Dragulescu" Institute of Chemistry, 24 M. Viteazu Avenue,Timisoara 300223, Romania, [e-mail] Keywords: antiviral, drug repurposing, machine learning # Content The repository is organized into the following folders: code – contains the Python source code of the prototype.pred-dc – contains the predictions generated on the DrugCentral dataset by the first level of the prototype.pred-dc-ml – contains the predictions on the DrugCentral dataset produced by the second level of the prototype, which is based on a meta-learner.source dataset – contains the original datasets downloaded from the ChEMBL and DrugCentral sources.source of figures – contains the original files used to generate the figures included in the manuscript.train-ch – contains the training data derived from the ChEMBL dataset for the first level of the prototype.train-ch-ml – contains the training data generated by the second level of the prototype, based on the meta-learner. # Requirements For CPU processing- Python 3.10.10- TensorFlow 2.10.0- Tested on Windows 10, Windows 11 For GPU processing:- Python 3.10.10- Nvidia Cuda 11.8.0- cudnn 8.6.0.163- tensorflow-2.10.0-cp310-cp310-win_amd64.whl- Tested on Windows 10, Windows 11 # Installation In the root folder of the uncompressed archive you have to run the following commands: # For CPU processingpip install tensorflow-cpu==2.10.0pip install -r requirements.txt # For GPU processing:pip install tensorflow-2.10.0-cp310-cp310-win_amd64.whlpip install -r requirements.txt # Running the Experiment Step 1: Training the Base Models In the root folder of the uncompressed archive you have to run the following commands: run-l1.bat- Trains the 10 base models and generates first level predictions.- The training is made on 9 use case files coresponding to the 3 enzymes x 3 descriptors.- The models train on the data from ChEMBL database located in ./train-ch/data/ folder. - The trained models predict the inputs from DrugCentral database located in ./pred-dc/data/ folder. python ./code/models/metric-performance.py- Computes the performance metrics. python ./code/plot-performance-metrics.py - Compiles the tables with the performance metrics data. python ./code/plot-met-dist-histplot.py - Plots the performance metrics distribution histogram. # Output Step 1 - The predictions from ChEMBL database are located in ./train-ch/predictions/ folder.- The predictions from DrugCentral database are located in ./pred-dc/predictions/ folder.- The performance metrics from ChEMBL are located in ./train-ch/metrics/ folder.- The performance metrics plots from ChEMBL are located in ./train-ch/metrics/metrics-plots/ folder. # Running the Experiment Step 2 Training the Meta-Learner run-l2.bat- Trains the metalearner on 9578 base level model predictions with varying hyperparameters.- The models train on the input data processed from the ChEMBL predictions located in ./train-ch-ml/data/ folder. - The trained models predict the input data processed from DrugCentral predictions located in ./pred-dc-ml/data/ folder. # Output Step 2 - The predictions of ChEMBL database are located in ./train-ch-ml/predictions/ folder.- The predictions of DrugCentral database are located in ./pred-dc-ml/predictions/ folder. # Notes If a GPU is available, TensorFlow 2.10 will use it automatically.If not, it will fall back to CPU execution.The code was tested using both TensorFlow CPU and GPU builds.The run-all.bat script runs both steps in a single command.