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Call for papers - Artificial intelligence for drug design

Guest Editors

Xing Chen, PhD, Jiangnan University, China
Yifeng Li, PhD, Brock University, Canada

Submission Status: Open   |   Submission Deadline: 10 August 2024

 
BMC Bioinformatics is calling for submissions to our Collection on ''Artificial intelligence for drug design''.

This collection welcomes submissions about the development and/or improvement of AI approaches including, but not limited to, deep learning models, (deep) generative models, reinforcement learning, computational intelligence, and foundation/generalist models.

Meet the Guest Editors

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Xing Chen, PhD, Jiangnan University, China

Xing Chen is a Professor of Jiangnan University. He is named Highly Cited Researcher by Clarivate Analytics in 2019, 2020, 2021 and 2022, Highly Cited Chinese Researchers by Elsevier in 2020, 2021 and 2022, and World's Top 2% Scientists released by Stanford University. He serves as handling editor of Briefings in Bioinformatics and associate editor of several academic journals, such as IEEE Journal of Biomedical and Health Informatics, Molecular Therapy-Nucleic Acids, Journal of Cellular and Molecular Medicine, BMC Bioinformatics, Interdisciplinary Sciences-Computational Life Sciences. He also serves as editorial board member of 9 well-known SCI journals such as Computers in Biology and Medicine, International Journal of Biological Sciences.

Yifeng Li, PhD, Brock University, Canada

Yifeng Li is an Assistant Professor and Canada Research Chair (Tier 2) in Machine Learning for Biomedical Data Science at the Department of Computer Science, Department of Biological Sciences, and Centre for Biotechnology, Brock University, Canada. During 2015-2019, he was a Research Officer at the Digital Technologies Research Centre, National Research Council Canada (NRC). He received the NRC Rising Star Award in 2018.  Prior to his joining to NRC, he was a post-doctorate at the Wasserman Laboratory of the Centre for Molecular Medicine and Therapeutics, University of British Columbia, Canada. He obtained his Ph.D. in Computer Science, from the University of Windsor, Canada, in 2013. His doctoral dissertation was recognized by a Gold Medal from the Governor General of Canada. His research interests include bioinformatics, chemoinformatics, drug design, neural networks, machine learning, data science, and optimization.


About the Collection

BMC Bioinformatics is calling for submissions to our Collection on ''Artificial intelligence for drug design''. 

Drug discovery is a time-consuming and expensive process, taking an average of more than 10 years and one billion US dollars to deliver a new drug into patients’ hands. Among the steps in drug discovery, drug design is a critical part in developing novel molecules that can bind the target(s) of interest. From the computational perspective, the chemical space is too vast for the identification of promising drug candidates through the enumeration of each molecular structure. Furthermore, drug design can be modeled as a many-objective optimization process, as not only must a candidate interact with the target, but it also has to be synthesizable and fulfill ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties. Many drug development projects declare failure in later stages due to severe side effects caused by the drug candidates under investigation. The rise of artificial intelligence (AI) techniques and the accumulation of extensive molecular datasets provide us with a great opportunity to revolutionize this field by innovative representation, prediction, and generative approaches. This collection welcomes submissions about the development and/or improvement of AI approaches including, but not limited to, deep learning models, (deep) generative models, reinforcement learning, computational intelligence, and foundation/generalist models, to address challenges in the following (but not exclusive to) tasks: 

•            Drug target interaction prediction
•            Drug combination prediction
•            Drug response prediction
•            Molecular representation learning
•            Small-molecule drug design
•            Lead optimization
•            Protein and antibody design
•            Aptamer design
•            Protein-ligand docking
•            Active site prediction
•            Protein tertiary structure prediction
•            Drug repurposing
•            ADMET property prediction
•            Side-effect prediction
 

Image credit: Grycaj / stock.adobe.com

  1. The identification of drug side effects plays a critical role in drug repositioning and drug screening. While clinical experiments yield accurate and reliable information about drug-related side effects, they ...

    Authors: Wuyong Liu, Jingyu Zhang, Guanyu Qiao, Jilong Bian, Benzhi Dong and Yang Li
    Citation: BMC Bioinformatics 2024 25:196
  2. The prediction of cancer drug response is a challenging subject in modern personalized cancer therapy due to the uncertainty of drug efficacy and the heterogeneity of patients. It has been shown that the chara...

    Authors: Chuanqi Lao, Pengfei Zheng, Hongyang Chen, Qiao Liu, Feng An and Zhao Li
    Citation: BMC Bioinformatics 2024 25:105
  3. The Drug–Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention....

    Authors: Alireza Dehghan, Karim Abbasi, Parvin Razzaghi, Hossein Banadkuki and Sajjad Gharaghani
    Citation: BMC Bioinformatics 2024 25:48

Submission Guidelines

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This Collection welcomes submission of original Research Articles, Software and Database articles. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select ''Artificial intelligence for drug design'' from the dropdown menu.

Articles will undergo the journal’s standard  peer-review process and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.