Automated drug design for druggable target identification using integrated stacked autoencoder and hierarchically self-adaptive optimization

Abstract excerpt

<title>Abstract</title> <p>Drug classification and target identification are crucial yet challenging steps in drug discovery. Existing methods often suffer from inefficiencies, overfitting, and limited scalability. Traditional approaches like support vector machines and XGBoost struggle to handle large, complex pharmaceutical datasets effectively. Deep learning models, while powerful, face challenges with interpr...

Topics

Open a Topic to create a Post that cites this publication.

• Bioinformatics and Genomic Networks
• Computational Drug Discovery Methods
• Data Mining
• Disease
• Drug Repositioning
• Environment
• High-Throughput Screening Assays
• Neural Networks, Computer

Identifiers and source

Literature Corpus work

5db585e6-1098-57d0-8760-041761ec9eb1

DOI

10.21203/rs.3.rs-5709513/v1