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Molecules
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Computational Approaches for Drug and Protein Design
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Computational Approaches for Drug and Protein Design

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 1226

Editors

Dr. Shirin Jamshidi

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Guest Editor
Faculty of Life Sciences & Medicine, King's College London, London, UK
Interests: machine learning; data analysis; molecular dynamics simulations; computational drug design and development; molecular docking and virtual screening; protein structure; function and dynamics
Special Issues, Collections and Topics in MDPI journals
Dr. Francisco Torrens

E-Mail Website
Guest Editor
Institut Universitari de Ciencia Molecular, Edifici d'Instituts de Paterna, P. O. Box 22085, E-46071 Valencia, Spain
Interests: theoretical chemistry; physical chemistry; mathematical chemistry; computational chemistry; molecular modelling; simulation and design; computer-aided drug design and development; molecular graphics and representation of molecular properties
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Todor Dudev

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Guest Editor
Faculty of Chemistry and Pharmacy, Sofia University "St. Kliment Ohridski", 1164 Sofia, Bulgaria
Interests: computational chemistry/biochemistry/biophysics; molecular modeling; metals in biology and medicine
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Prof. Dr. Gloria Castellano

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Guest Editor
Centro de Investigación Traslacional San Alberto Magno (CITSAM), Catholic University of Valencia San Vicente Mártir, 46001 Valencia, Spain
Interests: theoretical chemistry; natural products; organic chemistry; phytochemistry; medicinal plant chemistry; food chemistry
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Prof. Dr. Jesus Vicente De Julián Ortiz

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Guest Editor
Molecular Topology and Drug Design Research Unit, Department of Physical Chemistry, Faculty of Pharmacy and Food Sciences, University of Valencia, Av. Vicent Andrés Estellés s/n, 46100 Valencia, Spain
Interests: molecular topology; topological indices; drug design; virtual combinatorial synthesis; virtual screening; QSAR; statistical validation; MEDT; drug delivery; protein structure
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational methods have revolutionized the fields of drug discovery and protein engineering, enabling the rapid identification of promising therapeutic candidates and the optimization of biomolecular interactions. Advances in molecular modelling, artificial intelligence, and high-throughput simulations provide unprecedented insights into drug–target interactions, protein stability, and binding affinities. This Special Issue aims to highlight cutting-edge computational approaches that enhance drug design and protein engineering, including molecular docking, molecular dynamics simulations, quantum mechanics/molecular mechanics (QM/MM) approaches, artificial intelligence-driven modelling, generative AI, and structure-based drug design. We welcome original research articles and reviews that explore novel computational strategies, algorithmic innovations, and applications in drug and protein design. By gathering contributions from researchers across disciplines, this Special Issue will provide a comprehensive overview of state-of-the-art computational methodologies and their impact on accelerating drug discovery and protein engineering.

Dr. Shirin Jamshidi
Dr. Francisco Torrens
Prof. Dr. Todor Dudev
Prof. Dr. Gloria Castellano
Prof. Dr. Jesus Vicente De Julián Ortiz
Guest Editors

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Molecules 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 2700 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

  • computational drug design
  • molecular docking and molecular dynamics simulations
  • generative-AI and machine learning in drug discovery
  • protein structure prediction
  • ligand-based/structure-based drug discovery
  • biomolecular modelling

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Published Papers (2 papers)

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Research

38 pages, 7038 KB  
Article
Non-Classical Binding Mechanisms of Ferrocene-Modified Imatinib and Nilotinib Analogues in BCR-ABL1 Kinase Revealed by Computational Analysis
by Rostislava Angelova, Georgi Stavrakov, Danislav S. Spassov, Georgi Momekov and Mariyana Atanasova
Molecules 2026, 31(12), 2156; https://doi.org/10.3390/molecules31122156 - 18 Jun 2026
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Abstract
Background: Ferrocene-containing compounds have gained attention in medicinal chemistry due to their unique redox and structural properties. This study investigates ferrocene-based analogues of imatinib and nilotinib to define their binding determinants within the ABL1 kinase domain using an integrated in silico approach, in [...] Read more.
Background: Ferrocene-containing compounds have gained attention in medicinal chemistry due to their unique redox and structural properties. This study investigates ferrocene-based analogues of imatinib and nilotinib to define their binding determinants within the ABL1 kinase domain using an integrated in silico approach, in relation to their previously reported cytotoxic activity. Methods: Ligand geometries were optimized at the B3LYP/def2-TZVP level with D3(BJ) dispersion and SMD solvation. Molecular docking against ABL1 (PDB ID: 2HYY) was performed using Glide SP, validated by re-docking and enrichment screening. Docked poses were refined using MM-GBSA (Prime, VSGB 2.1/OPLS4). The most active compounds (9 and 15a), together with the inactive control 15e, were subjected to three independent 500 ns molecular dynamics simulations (Desmond, OPLS4), followed by trajectory analysis including RMSD, RMSF, radius of gyration, SASA, and polar surface area. Results: Compounds 9 and 15a maintained stable binding within the ATP-binding pocket despite lacking the canonical hinge interaction with Met318, indicating hinge-independent binding. Their binding was mainly driven by interactions with Asp381 (DFG motif) and cation–π contacts with Lys271. In contrast, the compound 15e showed unstable binding, increased conformational flexibility, reduced pocket burial, and loss of key stabilizing interactions. Active compounds also preserved stable P-loop dynamics, with Tyr253 engagement suggesting a role in loop stabilization. Compound 9 exhibited the most constrained and reproducible binding mode among all analogues. Conclusions: Ferrocene-based analogues can sustain stable ABL1 binding via non-classical interaction networks independent of hinge recognition. The clear distinction between active compounds and the inactive analogue 15e supports the robustness of the proposed binding mode and provides a structural basis for their reported cytotoxic activity. These findings support further experimental evaluation of ferrocene-containing scaffolds as potential BCR-ABL1 inhibitors. Full article
(This article belongs to the Special Issue Computational Approaches for Drug and Protein Design)
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36 pages, 20098 KB  
Article
Pocket-Surface Discrete Differential Geometry as a Leakage-Robust Feature Class for Protein–Ligand Binding Affinity Prediction
by Mehmet Ali Balcı, Erbil Çetin, Gizem Calibasi-Kocal and Ömer Akgüller
Molecules 2026, 31(11), 1899; https://doi.org/10.3390/molecules31111899 - 1 Jun 2026
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Abstract
Protein–ligand binding affinity prediction underpins structure-based drug discovery, yet random partitions of public benchmarks overestimate generalisation due to protein-family and ligand leakage, and the marginal value of explicit pocket-geometry descriptors over atom-level graph neural networks remains unclear. We computed a 59-dimensional discrete differential [...] Read more.
Protein–ligand binding affinity prediction underpins structure-based drug discovery, yet random partitions of public benchmarks overestimate generalisation due to protein-family and ligand leakage, and the marginal value of explicit pocket-geometry descriptors over atom-level graph neural networks remains unclear. We computed a 59-dimensional discrete differential geometry descriptor on the ligand-aware solvent-excluded surface of 3285 PDBBind v2020 complexes, combining curvature distributions, the leading sixteen Laplace–Beltrami eigenvalues and a ten-point heat-kernel signature, and evaluated it in gradient-boosted tree pipelines across progressively stricter split regimes and two leak-proof external benchmarks, together with four mechanistically distinct injection strategies in a SchNet-style graph neural network. The descriptor lifted Pearson correlations by 0.111 on cluster-disjoint testing, 0.258 on LP-PDBBind DataSAIL S2 and 0.365 on CASF-2016, while in isolation reaching 0.456 to 0.594 on external benchmarks, on a par with X-Score and AutoDock Vina (version 1.2). TreeSHAP attribution localised the dominant signal to the heat-kernel signature. The four graph neural network injection strategies produced no statistically significant lift, indicating that distance-based message passing on atomic coordinates already captures much of the geometric content. Pocket-surface discrete differential geometry, therefore, offers an interpretable, leakage-robust and lightweight feature class for early-stage virtual screening, and motivates hybrid mesh-to-atom architectures. Full article
(This article belongs to the Special Issue Computational Approaches for Drug and Protein Design)
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