Introduction
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.
Keywords
AI & Antiviral Drug Development, AI & Antibacterial Drug Development, AI & druggability, R&D of Anti-Infective Drugs, Machine Learning & Drug Development, AI & Drug Resistance Mechanisms.
Sub-topics
AI-Enabled Antiviral Drug Development
- AI for target identification and inhibitor discovery against viruses including COVID-19, influenza, HIV, HBV, and HCV
- Generative AI for designing novel antiviral small molecules, peptides, and nucleic acid drugs
- AI for predicting viral mutation, escape mechanisms, and broad-spectrum antiviral strategies
- AI-driven drug repurposing, combination therapy, and druggability optimization for antiviral drugs
- Multimodal AI integrating viral structure, host factors, and pathway networks for antiviral drug development
AI-Enabled Antibacterial Drug Development
- De novo design of novel antibiotics against drug-resistant bacteria (MRSA, carbapenem-resistant Enterobacteriaceae, Acinetobacter baumannii) via generative AI
- Rational design, optimization, and mechanism analysis of antimicrobial peptides (AMPs) using AI
- Machine learning for predi cting antibacterial activity, bactericidal mechanisms, and bacterial membrane disruption activity
- AI-enabled mining of novel antibacterial targets and anti-virulence/anti-quorum sensing drugs
- AI-accelerated virtual screening, synthetic feasibility prediction, and in vivo efficacy evaluation of antibacterial drugs
AI-Enabled Antifungal and Antiparasitic Drug Development
- AI-driven discovery and optimization of novel antifungal, antimalarial, and antiparasitic molecules
- AI research on pathogen-host interactions, drug resistance evolution, and precision therapy
AI Methodologies and Platforms
- Dedicated AI models, datasets, open-source tools, and evaluation benchmarks for anti-infective research
- AI drug discovery strategies under small-sample and low-data conditions
- Applications of explainable AI (XAI) in anti-infective drug development
Clinical Translation and Regulation
- AI support for clinical trial design, dose optimization, and real-world studies of anti-infective drugs
- AI applications in drug resistance surveillance, infection diagnosis, and precision medicine
