Artificial Intelligence-Powered Drug Discovery and Development
A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Chemical Biology".
Deadline for manuscript submissions: closed (15 July 2021) | Viewed by 531
Special Issue Editor
Interests: aging; exposome and exposomics; Alzheimer’s disease; Parkinson’s disease; depression; artificial intelligence; machine and deep learning; big data analytics; blockchain; stigma, socially assistive robotics; virtual/augmented/mixed reality; cancer
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Special Issue Information
Dear Colleagues,
Drug development is an expensive and time-consuming process. Compounded with the fact that few drug candidates end with a viable product, tools that can reduce the number of resources spent developing unsuccessful drug candidates are valuable. There are many facets that a successful drug must account for; a drug must not only be effective in altering the target’s activity, but also have favorable pharmacokinetic properties, minimal off-target effects and toxicity, etc. Artificial intelligence (AI)-powered in silico methods that utilize machine learning (ML) and deep learning (DL) algorithms to screen and/or generate compounds can be designed to account for these and a myriad of other chemical features, facilitating either the filtration or generation of compounds that meet the criteria for a viable drug. In vitro and in vivo validations are still necessary parts of the process, of course, but ML/DL models can be a substantial boon to the industry by providing an advantageous starting point in the drug development process.
This Special Issue will focus on advances in AI-powered solutions in the field of drug discovery and development. Both review and original research manuscripts are welcome.
Dr. Xudong Huang
Guest Editor
Manuscript Submission Information
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Keywords
- drug discovery
- artificial intelligence
- machine learning
- deep learning
- virtual screening
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