Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence II
A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Bioinformatics and Systems Biology".
Deadline for manuscript submissions: closed (1 August 2023) | Viewed by 17469
Special Issue Editors
Interests: bioinformatics; drug design; AI drug; protein dynamics; personal drug
Special Issues, Collections and Topics in MDPI journals
Interests: biophysical chemistry; drug repurposing and molecular modeling; computational chemistry; materials and multi-scale modeling
Special Issues, Collections and Topics in MDPI journals
Interests: biomedical informatics; computational genomics; machine learning and drug design; precision medicine
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning and drug design; computational structural biology; cancer genomics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Following a very successful first run, we are pleased to announce the launch of the second edition of a Special Issue on “Advances in Drug Design and Development for Human Therapeutics Using Artificial Intelligence”.
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. 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, also including the use of AI in drug design, poly-pharmacology, drug repositioning, drug screening, target identification, drug resistance prediction, and chemical synthesis.
Prof. Dr. Dongqing Wei
Prof. Dr. Gilles Peslherbe
Dr. Gurudeeban Selvaraj
Dr. Yanjing Wang
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biomolecules is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
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