Submission Tilte
Artificial Intelligence Empowers the Research and Development of Anti-Infective Drugs: From Target Discovery and Molecular Design to Clinical Translation
Submission Abstract:
The global crisis of drug resistance in bacteria, viruses, fungi, and parasites is becoming increasingly severe. Traditional anti-infective drug development faces bottlenecks such as long cycles, high costs, low success rates, and a shortage of novel structures. Artificial intelligence (AI) and machine learning are fundamentally reshaping the drug discovery and development chain, demonstrating disruptive value in target mining, virtual screening, de novo design, druggability optimization, resistance mechanism prediction, and clinical translation.
This special issue focuses on AI-driven innovation in anti-infective drugs and systematically collects cutting-edge research and reviews, with emphasis on:
- Applications of AI in the entire RD process of antiviral, antibacterial, antifungal, and antiparasitic drugs
- Generative AI, graph neural networks, large language models, and deep learning for the design of novel anti-infective molecules
- Multimodal AI integrating structural biology, omics, and clinical data to accelerate candidate molecule discovery
- AI-enabled analysis of pathogen drug resistance mechanisms and prediction of drug susceptibility and combination therapies
- AI-driven research on druggability, ADMET, toxicology, and precision medicine of anti-infective drugs
- Closed-loop computation and experimentation: in vitro/in vivo activity validation of AI-designed molecules
This special issue aims to build an interdisciplinary platform for computational pharmacy, microbiology, medicinal chemistry, and clinical medicine, promote the practical implementation of AI technologies, and provide new paradigms, strategies, and molecules to address the global crisis of infectious diseases and drug resistance.