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Biophysica
Special Issues
Biophysical Insights into Small Molecule Inhibitors
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Biophysical Insights into Small Molecule Inhibitors

A special issue of Biophysica (ISSN 2673-4125).

Deadline for manuscript submissions: 30 April 2026 | Viewed by 356

Special Issue Editor

Dr. Ossama Daoui

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Guest Editor
Laboratory of Engineering, Systems and Applications, National School of Applied Sciences, Sidi Mohamed Ben Abdellah-Fez University, Fez, Morocco
Interests: molecular modeling; drug design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to highlight recent advances in the study of small molecule inhibitors through a multidisciplinary biophysical lens. Emphasis will be placed on computational and experimental strategies that elucidate molecular recognition, binding mechanisms, and structure–activity relationships (SAR). Topics of interest include molecular dynamics simulations, free energy calculations, docking, QSAR modeling, and innovative descriptor-based approaches for rational drug design. Studies integrating traditional methods with novel computational or imaging-based descriptor extraction are particularly welcome, as they offer new perspectives for the characterization and optimization of inhibitors. Contributions spanning theory, methodology, and applied case studies are encouraged, with the ultimate goal of advancing drug discovery and providing deeper insights into molecular interactions.

Dr. Ossama Daoui
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 250 words) can be sent to the Editorial Office for assessment.

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. Biophysica is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). 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.

Keywords

  • small molecule inhibitors
  • biophysics
  • molecular dynamics
  • docking
  • QSAR modeling
  • molecular descriptors
  • structure–activity relationships
  • rational drug design

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Published Papers (1 paper)

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Research

29 pages, 11812 KB  
Article
Predicting Antiviral Inhibitory Activity of Dihydrophenanthrene Derivatives Using Image-Derived 3D Discrete Tchebichef Moments: A Machine Learning-Based QSAR Approach
by Ossama Daoui, Achraf Daoui, Mohamed Yamni, Marouane Daoui, Souad Elkhattabi, Samir Chtita and Chakir El-Kasri
Biophysica 2026, 6(1), 1; https://doi.org/10.3390/biophysica6010001 - 23 Dec 2025
Viewed by 198
Abstract
Making advancements in Quantitative Structure-Activity Relationship (QSAR) modeling is crucial for predicting biological activities in new compounds. Traditional 2D-QSAR and 3D-QSAR methods often face challenges in terms of computational efficiency and predictive accuracy. This study introduces a machine learning approach using 3D Discrete [...] Read more.
Making advancements in Quantitative Structure-Activity Relationship (QSAR) modeling is crucial for predicting biological activities in new compounds. Traditional 2D-QSAR and 3D-QSAR methods often face challenges in terms of computational efficiency and predictive accuracy. This study introduces a machine learning approach using 3D Discrete Tchebichef Moments (3D-DTM) to address these issues. The 3D-DTM method offers efficient computation, robust descriptor generation, and improved interpretability, making it a promising alternative to conventional QSAR techniques. By capturing global 3D shape information, this method provides better representation of molecular interactions essential for biological activities. We applied the 3D-DTM model to a dataset of 46 molecules derived from the Dihydrophenanthrene scaffold, screened against the enzymatic activity of 3-chymotrypsin-like protease, a key antiviral target. Principal Component Analysis and k-means clustering refined descriptors, followed by stepwise Multiple Linear Regression (step-MLR), Partial Least Squares Regression (PLS-R), and Feed-Forward Neural Network (FFNN) techniques for 3DTMs-QSAR model development. The results showed high correlation and predictive accuracy, with significant validation from internal and external tests. The step-MLR model emerged as the optimal method due to its balance of predictive power and simplicity. Validation through y-Randomization and applicability domain analysis confirmed the model’s robustness. Virtual screening of 100 novel compounds identified 32 with improved pIC50 values. This study highlights the potential of 3D-DTMs in QSAR modeling, providing a scalable and reliable tool for computational chemistry and drug discovery. A user-friendly software tool was also developed to facilitate 3D-DTM extraction from input 3D molecular images. Full article
(This article belongs to the Special Issue Biophysical Insights into Small Molecule Inhibitors)
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