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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) belonging to the section "Computational and Theoretical Chemistry".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2007

Editors

Dr. Shirin Jamshidi

E-Mail Website
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

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Gloria Castellano

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Jesus Vicente De Julián Ortiz

E-Mail Website
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 (4 papers)

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Research

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33 pages, 14775 KB  
Article
Mutation-Aware Machine Learning Framework for Predicting Binding Affinity of Nirmatrelvir Analogs Targeting Coronavirus Main Proteases
by Md Saidur Rahman, Md Mehedi Hasan and Shahidul M. Islam
Molecules 2026, 31(17), 2949; https://doi.org/10.3390/molecules31172949 - 22 Aug 2026
Viewed by 357
Abstract
The emergence of resistance-associated mutations in coronavirus main protease (Mpro) poses a significant challenge to the development of broad-spectrum antiviral therapeutics. In this study, we improved and accelerated a mutation-aware machine learning (ML) framework to predict the binding score of Nirmatrelvir analogue ligands [...] Read more.
The emergence of resistance-associated mutations in coronavirus main protease (Mpro) poses a significant challenge to the development of broad-spectrum antiviral therapeutics. In this study, we improved and accelerated a mutation-aware machine learning (ML) framework to predict the binding score of Nirmatrelvir analogue ligands against wild-type and mutant MERS-CoV Mpro. A library of 15,889 Nirmatrelvir derivatives generated through systematic scaffold modification was docked against the wild-type and five variants of the Mpro, producing a total of 95,334 structural and docking score datasets of these protein–ligand complexes. During the ML model development phase, ligand effects were learned from RDKit molecular descriptors and graph-based representations, and the mutation-induced effects were captured through delta-encoded physicochemical properties (hydrophobicity, charge, aromaticity, and polarity) of the active-site residues. Among the evaluated models, the CatBoost regressor tree-based algorithm achieved the lowest mean absolute error (MAE) value of 0.23 Kcal/mol and an R2 of 0.87. Further improvement was achieved by creating a weighted ensemble model combining the CatBoost regressor, XGBoost and LightGBM regressor, resulting in a prediction accuracy with a MAE of 0.19 Kcal/mol and an R2 of 0.90 relative to docking scores. Model robustness was further evaluated through random-, ligand group- and scaffold group- K-fold cross-validation along with their Y-randomization. Moreover, the models were also tested with a new set of 1000 structurally diverse compounds. SHAP analysis was conducted, which identified 20 molecular descriptors critical for accurate predictions. The ensemble model accurately predicted the binding affinities of Nirmatrelvir and its four analogues (E1–E4), reproducing the experimental pIC50 trend and correctly identifying the most potent inhibitors. The ensemble model also showed consistent performance across all MERS-CoV Mpro variants, S147Y, S142G, L144A, S142G/S147Y, and S142G/L144A/S147Y, demonstrating its potential for rapidly discovering mutation-resistant antiviral drugs. Full article
(This article belongs to the Special Issue Computational Approaches for Drug and Protein Design)
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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
Viewed by 494
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
Viewed by 565
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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Review

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44 pages, 1320 KB  
Review
Molecular Docking of Natural Products: Critical Appraisal of Current Methodology and Practical Guidelines
by Almagul S. Makhmutova, Nazigul S. Remetova and Gulnissa K. Kurmantayeva
Molecules 2026, 31(18), 3228; https://doi.org/10.3390/molecules31183228 - 12 Sep 2026
Abstract
Molecular docking is among the most widely used techniques in structure-based drug discovery and has become integral to natural-product research. Despite substantial advances in computational algorithms and artificial intelligence, the methodological quality of published docking studies on natural compounds remains highly variable, and [...] Read more.
Molecular docking is among the most widely used techniques in structure-based drug discovery and has become integral to natural-product research. Despite substantial advances in computational algorithms and artificial intelligence, the methodological quality of published docking studies on natural compounds remains highly variable, and generally accepted methodological guidelines have yet to be established. This review critically evaluates current approaches to molecular docking of natural compounds, examines the specific features of different natural-product classes and protein targets, and offers practical recommendations for the design and validation of docking studies. The review covers the main stages of molecular docking, contemporary search algorithms and scoring functions, protein and ligand preparation, the fundamental limitations of the method, strategies for result validation, and the emerging role of artificial intelligence in computational molecular modeling. A central component is a systematic methodological audit of publications within a prespecified 2025 coverage window, identified by Scopus and PubMed searches executed in August 2026. The audit assessed methodological reporting completeness only and was not intended as a systematic review of biological or pharmacological findings. The audit comprised GPT-assisted structured coding of 1127 publications, detailed full-text assessment of a random sample of 80 studies drawn entirely from the same corpus, and independent human validation of 20 randomly selected Stage 2 publications. In the 80-publication full-text sample, redocking was reported in 7.5% of publications, a qualifying redocking RMSD below 2 Å in 6.2%, positive controls in 87.5%, molecular dynamics in 41.2%, MM/PBSA or MM/GBSA in 12.5%, interaction analysis in 98.8%, and ADMET assessment in 35.0%. Agreement between Stage 1 and Stage 2 was 98.3% across 525 paired criterion decisions. Independent human assessment of the 20-publication validation subsample agreed with Stage 2 in 135 of 140 criterion decisions (96.4%); the five discrepancies all involved AI-coded indeterminate/non-confirmed labels (Unconfirmed or N/A) that the human reviewer classified as No. On the basis of these findings, we propose a practical workflow aimed at improving the reproducibility and methodological rigor of molecular docking studies of natural compounds. A complementary targeted case-enriched validation of redocking and redocking-RMSD classification included 19 publications and two human reviewers. The reviewers reached identical classifications in all 38 criterion decisions; their consensus agreed with Stage 2 in 31 of 38 decisions (81.6%), with seven revisions across four publications. Because this sample was deliberately enriched for informative and ambiguous cases, it was used to examine classification boundaries rather than to estimate prevalence. Because PubMed retrieval was restricted to free full text and the Scopus search used restricted bibliographic fields, these reporting-completeness and validation frequencies primarily characterize an accessibility-enriched corpus and may not fully generalize to subscription-only or otherwise less-accessible journals. By combining a critical appraisal of current methods with a quantitative assessment of published studies and practical recommendations for standardizing the molecular docking of natural compounds, this review offers guidance for researchers in computer-aided drug design. Full article
(This article belongs to the Special Issue Computational Approaches for Drug and Protein Design)
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