Chemoinformatics and Drug Design, 2nd Edition
A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Medicinal Chemistry".
Deadline for manuscript submissions: 20 January 2025 | Viewed by 126
Special Issue Editor
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) and machine learning (ML) have revolutionized various scientific fields, with cheminformatics, drug discovery, and drug repurposing being at the forefront of these advancements. Compared with traditional computational methods, the integration of AI/ML technologies promises to significantly enhance the efficiency and accuracy of predicting drug–target–disease interactions and biological activities.
This Special Issue will explore the cutting-edge of artificial intelligence and machine learning technologies for cheminformatics, drug discovery, and drug repurposing. It aims to present AI/ML studies on developing innovative models and novel representation methods for chemical entities (e.g., chemical structures and names), biological entities (e.g., target sequences, structures, and names), and clinical diseases. Our focus will be on predictive models that enhance understanding and prediction of drug–target–disease interactions and biological activity. Contributions will emphasize the efficient utilization of AI/ML technologies in cheminformatics and drug discovery research, as well as practical applications for identifying new therapeutic uses for existing drugs. This collection will demonstrate AI’s transformative potential in accelerating drug discovery processes and improving drug repurposing efforts. We also welcome research articles on novel methods for knowledge graph-based modeling, text mining, as well as applications of large language models (LLM) in cheminformatics research.
Dr. Weifan Zheng
Guest Editor
Manuscript Submission Information
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Keywords
- artificial intelligence (AI)
- machine learning (ML)
- representation learning
- cheminformatics
- drug discovery
- drug repurposing
- biological activity prediction
- drug–target–disease interactions
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