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New Computational Methodologies for Computer-Aided Drug Design and Chemoinformatics

A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".

Deadline for manuscript submissions: closed (10 March 2024) | Viewed by 3475

Special Issue Editor

Special Issue Information

Dear Colleagues,

Chemoinformatics is a multidisciplinary area of research primarily engaged with the collection, deposition, retrieval, and analysis of information in order to address chemistry-related problems. The analysis of chemistry-related data can take many forms, with one of the most important ones being quantitative structure–activity relationship (QSAR). QSAR can be broadly defined as the usage of mathematical models to find correlations between molecular activities (defined in the broadest possible sense) and a set of structure-based descriptors. Starting with early studies by Hansch and co-workers, the field has rapidly evolved through many significant advances, including data curation, descriptor calculation, regression and classification algorithms (e.g., Machine Learning algorithms), and evaluation metrics. Over the years, QSAR models have been widely and successfully used in many research areas, including chemistry, biology, toxicology, and material sciences, to both analyse the factors affecting molecular properties and design new compounds.

The purpose of this Special Issue is to provide an overview of the state of the art in current chemoinformatics methodologies, with an emphasis on the QSAR, and to describe how these methodologies are used in molecular modeling and drug design. We welcome original research articles, review articles, and short communications on one or more of the following topics:

  • The development, implementation, and application of chemoinformatics databases;
  • The development and application of new chemoinformatics tools;
  • The development and application of new molecular descriptors;
  • The construction and visualization of, and navigation through the chemical space;
  • The development of new QSAR algorithms and workflows;
  • The application of chemoinformatics and QSAR methodologies in molecular modeling and drug design.

We hope that this Special Issue will serve as an entry point for newcomers into the exciting world of chemoinformatics/QSAR, as well as a valuable reference for more experienced practitioners in the field.

Prof. Dr. Hanoch Senderowitz
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. There is an Article Processing Charge (APC) for publication in this open access journal. For details about the APC please see here. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • chemoinformatics
  • machine learning
  • data mining
  • quantitative structure activity relationship (QSAR)
  • quantitative structure property relationship (QSPR)
  • computer aided drug design (CADD)
  • molecular descriptors
  • databases
  • chemical space
  • data visualization

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Published Papers (1 paper)

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15 pages, 319 KiB  
Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review
by Sarfaraz K. Niazi and Zamara Mariam
Int. J. Mol. Sci. 2023, 24(14), 11488; https://doi.org/10.3390/ijms241411488 - 15 Jul 2023
Cited by 8 | Viewed by 2897
In modern drug discovery, the combination of chemoinformatics and quantitative structure–activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the [...] Read more.
In modern drug discovery, the combination of chemoinformatics and quantitative structure–activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structure–activity relationships (SARs), are pivotal in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. Due to embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences. Full article
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