Call for papers - Big data and artificial intelligence for drug discovery

Guest Editors:
Yuemin Bian: Broad Institute of MIT and Harvard, USA
Michal Brylinski: Louisiana State University, USA

Submission Status: Open | Submission Deadline: 4 April 2024

BMC Pharmacology and Toxicology is calling for submissions to our Collection on big data and artificial intelligence for drug discovery. The use of big data and artificial intelligence (AI) in the pharmaceutical industry is rapidly growing. Among others, the speed of drug discovery and development aided by big data and AI is unprecedented, owing to its potential to accelerate the development of therapeutics. With the increase in volume of biological data available, machine learning algorithms trained on large sets of data can aid in the speed of drug discovery and personalized medicine.

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Meet the Guest Editors

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Yuemin Bian: Broad Institute of MIT and Harvard, USA

Yuemin Bian is a computational scientist in the Center for the Development of Therapeutics (CDoT) at the Broad Institute of MIT and Harvard. His research focuses on developing and integrating machine learning, computational chemistry, and cheminformatics technologies to facilitate early-stage drug discovery.

Michal Brylinski: Louisiana State University, USA

Michal Brylinski, an associate professor at LSU's Department of Biological Sciences and Center for Computation and Technology, specializes in computational drug discovery and design. His interdisciplinary team focuses on integrating multi-dimensional biological data into computer-aided drug discovery, utilizing high-level omics data and molecular modeling. They employ deep learning techniques to analyze and classify large-scale experimental and computer-generated biological data. Michal has authored 90+ publications, some featured on high-impact journal covers, and provides editorial and review services to scientific journals and funding agencies worldwide.

About the collection

BMC Pharmacology and Toxicology is calling for submissions to our Collection on big data and artificial intelligence for drug discovery.

The use of big data and artificial intelligence (AI) in the pharmaceutical industry is rapidly growing. Among others, the speed of drug discovery and development aided by big data and AI is unprecedented, owing to its potential to accelerate the development of therapeutics while maintaining low costs. AI has been, and continues to be used in several points of drug discovery. This includes rapid identification of potential targets and compounds, prediction of efficacy and toxicity and optimization of drug design. With the increase in volume of biological data available, machine learning algorithms trained on large sets of data can aid in the speed of drug discovery and personalized medicine.

BMC Pharmacology and Toxicology has launched this collection to encourage conversations on the intersection of big data and artificial intelligence and drug discovery. We welcome studies from diverse research backgrounds, and encourage studies including but not limited to AI-based drug design, optimization and screening platforms, novel computational methods for drug design. We also welcome studies exploring the issues and challenges surrounding implementation of big data and AI for drug discovery.

Image credit: metamorworks / Getty Images / iStock

There are currently no articles in this collection.

Submission Guidelines

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This Collection welcomes submission of original Research 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 "Big data and artificial intelligence for drug discovery" 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.