The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B).

The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B).

<p>The AUC-ROC curve of 5-fold cross-validation of the training dataset (A) and the confusion matrix entries for the training, validation, and test datasets (B).</p>

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Bibliographic Details
Main Author:	Muhammad Yasir (3555896) (author)
Other Authors:	Jinyoung Park (134860) (author), Eun-Taek Han (620126) (author), Jin-Hee Han (1314552) (author), Won Sun Park (14905755) (author), Mubashir Hassan (614591) (author), Andrzej Kloczkowski (612861) (author), Wanjoo Chun (6752006) (author)
Published:	2024
Subjects:	Biochemistry Cell Biology Pharmacology Biotechnology Cancer Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified tnf -&# 945 active soluble form 7 cell confirmed predictive model based learning model followed graphconvmol model within molecular dynamics simulation extracted molecular features reference tace inhibitor key tace residues deep learning models tace inhibitory potential potential tace inhibitor decoy compounds specific trained model repurposing potential molecular docking tace ). specific targets integrated deep xlink "> using rdkit using bms therapeutic intervention subsequently used rheumatoid arthritis raw 264 promising target inflammatory response inflammatory mediators increasing utilization holds promise highly efficient e datasets e database drug repositioning deepchem framework crucial role converting pro converting enzyme computational results cheminformatics toolkit biological evaluation approved drugs approved drug also known
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