Call for papers - Computer-aided drug design

Guest Editors

Jorddy N. Cruz, PhD, Federal University of Pará, Brazil
Alexander Lachmann, PhD, Icahn School of Medicine at Mount Sinai, USA
Suraj N. Mali, PhD, School of Pharmacy, D.Y. Patil University (Deemed University), Navi Mumbai, India

Submission Status: Open | Submission Deadline: 12 May 2025

This BMC Methods Collection seeks to gather research on computer-aided drug discovery and development, encompassing AI, machine learning, and advanced computational methods. We invite researchers to submit their work, showcasing the latest advancements in computer-aided drug design, including molecular dynamics, virtual screening, computer modeling, and predictive toxicology. The Collection aims to advance our understanding of these methodologies, ultimately contributing to the acceleration of drug discovery and development processes.

New Content Item This Collection supports and amplifies research related to SDG 3: Good Health & Well-Being.

Submit to Collection

Meet the Guest Editors

Back to top

Jorddy N. Cruz, PhD, Federal University of Pará, Brazil

Dr Jorddy Neves Cruz is researcher in Federal University of Pará and Paraense Emílio Goeldi Museum. His research focuses on medicinal chemistry, with a special emphasis on medicinal plants; the evaluation of biological activities and pharmacological potential of natural and synthetic compounds; and molecular modeling approaches in the discovery of new drugs.

Alexander Lachmann, PhD, Icahn School of Medicine at Mount Sinai, USA

Dr Alexander Lachmann is a Research Assistant Professor at the Icahn School of Medicine at Mount Sinai, New York. He is a member of the Ma’ayan Laboratory in the Department of Pharmacological Sciences with a research focus on artificial intelligence and the development of large-scale cloud computing infrastructures. Dr Lachmann has developed popular statistical tools and online resources to analyze gene expression data, such as KEA and ChEA, and with ARCHS4, has created the largest homogeneously processed gene expression repository to date. Using unbiased, high-throughput data, Dr Lachmann developed novel statistical methods to infer gene annotations for understudied genes from druggable gene families such as kinases, GPCRs, and ion channels.

Suraj N. Mali, PhD, School of Pharmacy, D.Y. Patil University (Deemed University), Navi Mumbai, India

Dr Suraj Mali has a PhD in Pharmacy. He is a former analytical scientist at Dr Reddy's Laboratories, India and has an academic background in pharmaceutical science and technology from the Institute of Chemical Technology, India. Dr Mali serves as a respected reviewer for multiple scientific journals and was designated a Bentham Science Brand Ambassador for 2019–2020. He received the Institute of Chemical Technology’s (ICT) Masters Best Thesis Aditya Birla Award in 2019. With more than 95 international journal publications to his credit, his expertise is diverse, spanning molecular modeling, synthetic chemistry, phytochemistry, pharmacology, and analytics, with a focus on drug design and synthesis. A recent publication in Nature Scientific Reports highlights his work in identifying antimycobacterial agents using computational tools. Dr Mali was listed among the world’s top 2% of scientists by Stanford University, USA, in 2023.

About the Collection

Computer-aided drug design (CADD) has transformed modern drug discovery by employing computer techniques to find, develop, and assess biologically active compounds. It serves as a potent tool in expediting the early stages of chemical development and drug discovery processes. The techniques and tools utilized in CADD permeate all stages of the drug discovery pipeline, facilitating the identification and design of potential drug candidates. One of the primary advantages of CADD is its ability to predict the interactions between small molecules and biological targets with high accuracy. This predictive capability is achieved through the use of various computational techniques, including molecular docking, molecular dynamics simulations, and quantitative structure-activity relationship (QSAR) modeling.

Over the past two decades, advancements in drug design protocols, coupled with increased computational power, have enabled scientists to effectively generate disease-oriented solutions at reduced costs and within feasible timeframes. Advancing our collective understanding of computer-aided drug discovery and development is paramount for accelerating the identification of novel therapeutics, optimizing drug properties, and minimizing the time and resources required for drug development. Recent breakthroughs underscore the potential of artificial intelligence (AI) and machine learning in predicting molecular interactions, identifying drug targets, and optimizing lead compounds, thereby streamlining drug discovery processes and reducing associated costs. Furthermore, the integration of predictive toxicology models has bolstered the safety assessment of drug candidates, mitigating late-stage failures in drug development.

This Collection aims to compile research that encompasses various aspects of computer-aided drug design, molecular dynamics, virtual screening, computer modeling, and predictive toxicology, highlighting the latest advancements and methodologies in the field. Topics of interest include but are not limited to:

AI and machine learning in drug development
Molecular dynamics simulations
Molecular docking
Predictive toxicology in drug development
Virtual screening strategies
Virtual library design
Quantitative structure-activity relationship modeling
High-throughput screening
Lead optimization techniques
De novo design methodologies: ligand based drug design and structure based drug design
Other computational approaches within the realm of CADD

There are currently no articles in this collection.

Submission Guidelines

Back to top

This Collection welcomes submission of original Methodology and Protocol 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 "Computer-aided 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.