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Call for papers - Chemistry and machine learning

Guest Editors

Marcus Tullius Scotti, PhD, Federal University of Paraíba, Brazil
Renjith Thomas, PhD, St. Berchmans College, Mahatma Gandhi University, India

Submission Status: Open   |   Submission Deadline: 16 September 2024


BMC Chemistry is calling for submissions to our Collection on Chemistry and machine learning.

Machine learning has rapidly become a pivotal tool across the chemical and pharmaceutical sciences, revolutionizing our approach to research and discovery. This Collection aims to explore the wide-ranging applications of machine learning in chemistry, encompassing drug development, materials science, chemical synthesis, analytical chemistry, and more. A special focus will also be placed on work discussing the integration of computational methods and data-driven approaches to advance our understanding of chemical processes.

Meet the Guest Editors

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Marcus Tullius Scotti, PhD, Federal University of Paraíba, Brazil

Dr Marcus Tullius Scotti graduated from the Polytechnic School of the University of São Paulo (USP) with a degree in Chemical Engineering in 1999, and he worked in a Brazilian electronics and telecommunications services company called Gradiente (1999-2003). He did his specialization in industrial administration at the University of São Paulo (2001-2003). He holds a Master's (2005) and PhD (2008) degree in Organic Chemistry from the Chemistry Institute of the University of São Paulo (USP). He did postdoctoral research in cheminformatics at Nova University Lisbon, Portugal (2013). Dr Tullius Scotti is currently an Associate Professor in the Chemistry Department of the Federal University of Paraíba, where works since 2009. His research interests are in Cheminformatics applied to natural products databases, virtual Screening, QSAR, and chemotaxonomy.

He has published 308 papers, authored 19 book chapters, and edited two books. He has 3556 citations (Web of Science) and 5202 (Google Scholar), with an H-index=30 (Web of Science) and H-index=39 (Google Scholar), respectively.

Renjith Thomas, PhD, Mahatma Gandhi University, India

Dr Renjith Thomas is currently the Head of the Department of Chemistry and Director of the Centre for Theoretical and Computational Chemistry at St Berchmans College, Mahatma Gandhi University, Kerala, India. He is a theoretical and computational chemist and has contributed much to the scientific literature. Dr Renjith was born in 1980 in Changanassery, India, and studied BSc in Chemistry from St Berchmans College, MSc from Gandhigram University with a Gold Medal, and PhD in Theoretical Chemistry from Bharathidasan University. Recently, he has been interested in the evaluation of the nature of interaction between drug molecules and solvents and the development of a protocol to model the nature of drug-drug weak interactions. The other areas of interest are Chemical Physics, theoretical evaluation of the reaction mechanism, molecular structure, spectroscopy, weak interactions, drug design, and machine learning approaches.

He authored more than 144 research papers in different well-known journals, with a cumulative H-index of 36. He is an Associate Editor of the Journal of Computational Biophysics and Chemistry, Associate Editor of Frontiers in Chemistry: Theoretical Chemistry Division, Editorial Board Member of BMC Chemistry, and Editorial Board Member of Polytechnique (Elsevier), and a regular reviewer for about 40 journals. He was listed in the World’s top 2% of scientists (2021 to 2023) in Chemical Physics by Stanford University and Elsevier. He was elected as a fellow of the Royal Chemical Society, London, and executive council member of the Kerala Academy of Sciences. He is the recipient of the prestigious Prof Sivaprasad Award for the best college teacher in Kerala-2023.

About the Collection

BMC Chemistry is excited to invite researchers to contribute to a new Collection examining the intersection of Chemistry and machine learning.  

Machine learning has rapidly become a pivotal tool across the chemical and pharmaceutical sciences, revolutionizing our approach to research and discovery. This Collection aims to explore the wide-ranging applications of machine learning in chemistry, encompassing drug development, materials science, chemical synthesis, analytical chemistry, and more. A special focus will also be placed on work discussing the integration of computational methods and data-driven approaches to advance our understanding of chemical processes.

This Collection welcomes original research that contributes to our understanding of the synergies between chemistry and machine learning:

  • Drug Discovery and Design: Leveraging machine learning to discover and design new pharmaceutical compounds.
  • Materials Science: Using machine learning to predict material properties, accelerate discovery, and optimize synthesis.
  • Chemical Synthesis: Enhancing synthesis processes through machine learning algorithms.
  • Analytical Chemistry: Advancements techniques and instrumentation through machine learning.
  • Chemoinformatics: Data-driven approaches for data mining, informatics, and virtual screening.
  • Quantum Chemistry: Applications of machine learning to quantum chemistry simulations and predictions.
  • Molecular Modeling: Combining computational chemistry with machine learning for molecular modelling and simulations.
  • Automation and Robotics: Machine learning-driven automation in chemical laboratories.
  • Big Data in Chemistry: Handling and analyzing large-scale chemical datasets for insights and discoveries.


Image credit: Sergey Tarasov / stock.adobe.com

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 Chemistry and machine learning 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.