Article: Deep learning-based virtual screening system for drug molecules Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2026 Vol.18 No.8 pp.44 - 55 Abstract: In the field of drug discovery, traditional virtual screening methods face challenges of being time-consuming, costly, and limited in accuracy. To address this, this study developed a deep learning-based virtual screening system for drug molecules. By automatically learning key molecular features through graph neural networks, the system overcomes the limitations of traditional methods that rely on manual feature extraction, thereby capturing more complex structural information. Testing on the public benchmark Directory of Useful Decoys: Enhanced (DUD-E) demonstrates that this system achieves an area under the curve (AUC) of 0.889 while significantly reducing screening time - approximately 80% faster than conventional methods. This research provides an efficient solution for rapidly and accurately identifying potential drug candidates from vast compound libraries, paving the way for accelerated drug development. Inderscience Publishers - linking academia, business and industry through research

Title: Deep learning-based virtual screening system for drug molecules

Authors: Chuyue Zhang

Addresses: College of Pharmacy, China Pharmaceutical University, Nanjing, 211198, China

Abstract: In the field of drug discovery, traditional virtual screening methods face challenges of being time-consuming, costly, and limited in accuracy. To address this, this study developed a deep learning-based virtual screening system for drug molecules. By automatically learning key molecular features through graph neural networks, the system overcomes the limitations of traditional methods that rely on manual feature extraction, thereby capturing more complex structural information. Testing on the public benchmark Directory of Useful Decoys: Enhanced (DUD-E) demonstrates that this system achieves an area under the curve (AUC) of 0.889 while significantly reducing screening time - approximately 80% faster than conventional methods. This research provides an efficient solution for rapidly and accurately identifying potential drug candidates from vast compound libraries, paving the way for accelerated drug development.

Keywords: virtual screening; deep learning; drug discovery; graph neural networks; molecular characterisation.

DOI: 10.1504/IJRIS.2026.151726

International Journal of Reasoning-based Intelligent Systems, 2026 Vol.18 No.8, pp.44 - 55

Received: 16 Nov 2025
Accepted: 20 Dec 2025
Published online: 17 Feb 2026 *

Title: Deep learning-based virtual screening system for drug molecules

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