Molecular Informatics

Aims & Scope

Molecular Informatics is a peer-reviewed, international forum for publication of high-quality, interdisciplinary research on all molecular aspects of bio/cheminformatics and computer-assisted molecular design.

Molecular Informatics succeeded QSAR & Combinatorial Science in 2010.

Molecular Informatics presents methodological innovations that will lead to a deeper understanding of ligand-receptor interactions, macromolecular complexes, molecular networks, design concepts and processes that demonstrate how ideas and design concepts lead to molecules with a desired structure or function, preferably including experimental validation.

The journal's scope includes but is not limited to the fields of drug discovery and chemical biology, protein and nucleic acid engineering and design, the design of nanomolecular structures, strategies for modeling of macromolecular assemblies, molecular networks and systems, pharmaco- and chemogenomics, computer-assisted screening strategies, as well as novel technologies for the de novo design of biologically active molecules.

As a unique feature Molecular Informatics publishes so-called "Methods Corner" review-type articles which feature important technological concepts and advances within the scope of the journal.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	3.1
2024	2.80

SJR (SCImago Journal Rank)

Year	Value
2024	0.549

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	78

Journal Rank

Year	Value
2024	10597

Journal Citation Indicator

Year	Value
2024	469

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science
• Medline
• Embase

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Biochemistry, Genetics and Molecular Biology, Chemistry, Computer Science and Pharmacology, Toxicology and Pharmaceutics, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Best Practices for QSAR Model Development, Validation, and Exploitation

  Citation: 1561

  Authors: Alexander

• Free Energy Calculations by the Molecular Mechanics Poisson−Boltzmann Surface Area Method

  Citation: 946

  Authors: Nadine, Holger

• Deep Learning in Drug Discovery

  Citation: 591

  Authors: Erik, Jan A., Gisbert

• Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds

  Citation: 391

  Authors: Anh‐Tien, Francesco, Michael, Fuqiang, Artem

• Generative Recurrent Networks for <i>De Novo</i> Drug Design

  Citation: 333

  Authors: Anvita, Alex T., Berend J. H., Jens A., Petra, Gisbert

• <i>De Novo</i> Design of Bioactive Small Molecules by Artificial Intelligence

  Citation: 309

  Authors: Daniel, Lukas, Francesca, Gisbert

• QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction

  Citation: 298

  Authors: Alexey, Alexey, Dmitry, Vladimir

• Application of Generative Autoencoder in <i>De Novo</i> Molecular Design

  Citation: 298

  Authors: Thomas, Marcus, Ola, Jürgen, Hongming

• Network‐based Approaches in Pharmacology

  Citation: 269

  Authors: Baptiste, Karine, Pierre, Olivier

• QSAR Modeling is not “Push a Button and Find a Correlation”: A Case Study of Toxicity of (Benzo‐)triazoles on Algae

  Citation: 220

  Authors: Paola, Stefano, Partha Pratim, Simona, Chun Wei, Ester

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