Artificial Intelligence in Drug Design: Opportunities and Challenges
A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".
Deadline for manuscript submissions: closed (1 December 2023) | Viewed by 2603
Special Issue Editors
Interests: artificial intelligence; machine learning; deep learning; chemoinformatics; drug design; systems biology; single cell multiomics
Interests: ML and DL approaches for druggability prediction; QM fragmentation based approaches for druggability prediction; parallel virtual screening in HPCs and GPUs for accelerated drugs discovery; flexible docking assisted drugs discovery; atomistic simulation of virus-host cell interaction
Special Issue Information
Dear Colleagues,
In traditional methods of drug design, searching for a drug in a haystack that exhibits desired biological activities while conforming to safe pharmacological profiles can be long, costly, and challenging tasks. Recent advances in artificial intelligence (AI) have brought a revolution in today’s drug discovery process, ranging from target identification and lead searching, to safety profile prediction. Computational techniques, such as chemoinformatics, can be used to extract meaningful features from chemical structures of large compound databases. In conjunction with machine learning models, quantitative structure–activity relationships (QSARs) can be established to infer new drug activity, inverse drug design, and drug repurposing. Recent rises in deep learning and generative AI also saw their wide applicability in protein structure prediction, functional site identification, structure-based drug design (SBDD), and ligand-based drug design (LBDD). System-level information from clinical data and high-throughput experiments can likewise be leveraged via data-driven ways to minimize drug toxicity and accelerate drug approval. In recognition of the increasing influences of AI in drug design, researchers and drug designers from academia and the pharmaceutical industry are invited to contribute to this special Issue titled, “Artificial Intelligence in Drug Design: Opportunities and Challenges” to guide future development in this emerging field.
Dr. Yu-Chen Lo
Dr. Natarajan Arul Murugan
Guest Editors
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Keywords
- artificial intelligence
- machine learning
- deep learning
- chemoinformatics
- drug design
- systems biology bioinformatics multiomics
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