Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence
This special issue belongs to the section "Bioinformatics and Systems Biology".
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) and the related sub-technologies (machine learning and deep learning) are anticipated to make the development of novel therapeutics quicker, more effective, and inexpensive. AI can be applied to all the key areas of the pharmaceutical industries, such as drug discovery and development, drug repurposing, and improving productivity within a short period of time. The contemporary methods have shown promising results in facilitating the discovery of drugs to target different diseases. Thus, this Special Issue aims to present an overview of recent advances in computational modeling, machine learning, and deep learning to identify therapeutic targets, candidate drugs, molecular interactions, and their mechanisms of action. This Special Issue seeks high-quality original and review articles on these themes, including also the use of AI in drug design, poly-pharmacology, drug repositioning, drug screening, target identification, drug resistance prediction, and chemical synthesis.
Prof. Dongqing Wei
Prof. Dr. Gilles Peslherbe
Dr. Gurudeeban Selvaraj
Dr. Yanjing Wang
Guest Editors
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Keywords
- AI in a quantitative structure-activity relationship (QSAR)
- deep learning in drug discovery
- drug delivery and AI
- graph neural networks
- AI models for drug resistance prediction
- molecular dynamic simulations
- structure and ligand-based pharmacophore
- target protein structure prediction
- AI-based peptide inhibitor design
- AI models for drug property prediction
- AI-based webservers and drug databases
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Published Papers
Biomolecules - ISSN 2218-273X