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Pharmaceuticals
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Computer-Aided Drug Design and Drug Discovery, 2nd Edition
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Computer-Aided Drug Design and Drug Discovery, 2nd Edition

A special issue of Pharmaceuticals (ISSN 1424-8247). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: 25 April 2026 | Viewed by 5546

Special Issue Editors

Dr. Dragos Mihai

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Guest Editor
Faculty of Pharmacy, “Carol Davila” University of Medicine and Pharmacy, Bucharest, Romania
Interests: pharmacology; drug discovery; drug repurposing; virtual screening, small molecule drugs; neurodegenerative diseases; TRP channels
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. George Mihai Nitulescu

E-Mail Website
Guest Editor
Department of Pharmaceutical Chemistry, Faculty of Pharmacy, Carol Davila University of Medicine and Pharmacy, Traian Vuia 6, 020956 Bucharest, Romania
Interests: drug design; QSAR; molecular docking; anti-infective; MDR-bacteria
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

 Dear Colleagues,

Computer-aided methods play a crucial role in every stage of the discovery and development of drugs, enabling a more efficient and cost-effective approach. The integration of computational methods with experimental approaches has become indispensable in modern drug discovery. At a time when advancements in technology are revolutionizing the pharmaceutical industry, this Special Issue aims to highlight the latest developments in computer-aided drug design and drug discovery. The iterative process of designing, synthesizing, and testing new compounds can result in the identification of promising drug leads. This Special Issue will provide a platform for researchers to showcase cutting-edge research, innovative methodologies, and breakthroughs in pharmaceutical research that leverage computational approaches. It will explore various aspects of these areas, including, but not limited to, the following:

  • Novel computational techniques and algorithms in drug design
  • Artificial intelligence and machine learning in drug discovery
  • Molecular modeling and simulation for drug development
  • Virtual screening and high-throughput screening methods
  • Structure-based drug design and ligand–receptor interactions
  • CADD in pharmacokinetics (ADME prediction)
  • Target and off-target identification
  • Repurposing candidate prioritization
  • Optimization strategies for lead compounds
  • Predictive modeling and toxicology assessments
  • Case studies and successful applications of computer-aided drug design.

We invite you to contribute to this Special Issue by submitting original research, review articles, or perspectives that encapsulate your expertise and insights. Your contribution will provide readers with valuable knowledge and inspire further advancements in the field.

Dr. Dragos Paul Mihai
Prof. Dr. George Mihai Nitulescu
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Pharmaceuticals is an international peer-reviewed open access monthly 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 2900 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

  • chemoinformatics
  • bioinformatics
  • molecular modeling
  • virtual screening
  • molecular docking
  • quantitative structure–activity relationship (QSAR)
  • pharmacophore modeling
  • target identification
  • repurposing
  • ADME-T prediction

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Related Special Issue

Published Papers (5 papers)

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Research

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34 pages, 3834 KB  
Article
Design, Synthesis, and Evaluation of Pyrrole-Based Selective MAO-B Inhibitors with Additional AChE Inhibitory and Neuroprotective Properties Identified via Virtual Screening
by Emilio Mateev, Samir Chtita, Ekaterina Pavlova, Ali Irfan, Diana Tzankova, Shubham Sharma, Borislav Georgiev, Alexandrina Mateeva, Georgi Momekov, Maya Georgieva, Alexander Zlatkov and Magdalena Kondeva-Burdina
Pharmaceuticals 2025, 18(11), 1677; https://doi.org/10.3390/ph18111677 - 5 Nov 2025
Viewed by 503
Abstract
Background: Virtual screening is a widely adopted technique for the discovery of novel pharmacologically active compounds; however, the risk of identifying false positive hits remains a major challenge. Aim: The aim of this study was to perform a validated structure-based drug design screening [...] Read more.
Background: Virtual screening is a widely adopted technique for the discovery of novel pharmacologically active compounds; however, the risk of identifying false positive hits remains a major challenge. Aim: The aim of this study was to perform a validated structure-based drug design screening to discover multitarget pyrrole-based molecules as selective dual-acting monoamine oxidase (MAO) and acetylcholinesterase (AChE) inhibitors. Methods: The study employed validated docking protocols using Glide (Schrödinger) and GOLD (CCDC), integrating ligand enrichment analysis and robust Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) rescoring. These methods were applied to a custom-designed database of pyrrole-based compounds. The top-ranked hits were synthesized and validated through in vitro tests, demonstrating significant inhibitory activities against MAO-A, MAO-B, AChE, and Butyrylcholinesterase (BChE). Results: The docking protocols achieved favorable hit rates, with 25.93% for AChE inhibitors and 44.44% for MAO-B inhibitors. Additionally, structure–activity relationship analysis revealed key substituent effects that significantly influence binding affinity and selectivity. Two compounds, EM-DC-19 (2-(2,5-dimethyl-1H-pyrrol-1-yl)-3-(2H-imidazol-4-yl)propanoic acid) and EM-DC-27 ([4-(2,5-dimethyl-1H-pyrrol-1-yl)phenyl]acetic acid), were identified as selective MAO-B inhibitors with additional moderate AChE inhibitory activity, demonstrating IC50 values of 0.299 ± 0.10 µM and 0.344 ± 0.10 µM against MAO-B, and 76.15 ± 6.12 µM and 375.20 ± 52.99 µM against AChE, respectively. The absence of statistically significant inhibitory effects of these lead compounds on MAO-A and BChE (IC50 > 100 µM) underscores their selective inhibitory activity towards MAO-B and AChE. Furthermore, both compounds demonstrated low neurotoxicity and significant neuroprotective and antioxidant effects in rat brain synaptosomes, mitochondria, and microsomes. These effects were particularly evident in models of 6-hydroxydopamine-induced neurotoxicity (6-OHDA) and oxidative stress induced by tert-butyl hydroperoxide and Fe2+/ascorbic acid. Conclusions: The findings suggest that these multitarget compounds hold promise for further development, with potential for structural modifications to enhance their enzyme inhibitory and neuroprotective properties. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery, 2nd Edition)
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26 pages, 7708 KB  
Article
Computational Development of Multi-Epitope Reovirus Vaccine with Potent Predicted Binding to TLR2 and TLR4
by Abdullah Al Noman, Abdulrahman Mohammed Alhudhaibi, Pranab Dev Sharma, Sadia Zafur Jannati, Tahamina Akhter, Samira Siddika, Kaniz Fatama Khan, Tarek H. Taha, Sulaiman A. Alsalamah and Emad M. Abdallah
Pharmaceuticals 2025, 18(11), 1632; https://doi.org/10.3390/ph18111632 - 29 Oct 2025
Viewed by 800
Abstract
Background: Mammalian orthoreovirus is a ubiquitous double-stranded RNA virus that causes mild respiratory and enteric infections, primarily in infants and young children. Its significant environmental stability and association with conditions like celiac disease highlight an unmet medical need, as no licensed vaccine or [...] Read more.
Background: Mammalian orthoreovirus is a ubiquitous double-stranded RNA virus that causes mild respiratory and enteric infections, primarily in infants and young children. Its significant environmental stability and association with conditions like celiac disease highlight an unmet medical need, as no licensed vaccine or antiviral treatment currently exist. Methods: An immunoinformatics-driven approach was employed to design a multi-epitope vaccine. The highly antigenic inner capsid protein Sigma-2 was used to predict cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), and linear B cell epitopes using NetCTL, NetMHCpan, NetMHCIIpan, and IEDB tools. Selected epitopes were fused with appropriate linkers. The construct’s antigenicity, allergenicity, and physicochemical properties were evaluated. The tertiary structure was predicted with AlphaFold2, refined, and validated. Molecular docking with TLR2 and TLR4 was performed using HDOCK, and immune response simulation was conducted with C-ImmSim. Finally, the sequence was codon-optimized for E. coli expression using JCat. Results: The final vaccine construct comprises one CTL, four HTLs, and one B cell epitope. It is antigenic (VaxiJen score: 0.5026), non-allergenic, and non-toxic and possesses favorable physicochemical properties, including stability (instability index: 32.28). Molecular docking revealed exceptionally strong binding to key immune receptors, particularly TLR2 (docking score: −324.37 kcal/mol). Immune simulations predicted robust antibody production (elevated IgM, IgG1, and IgG2) and lasting memory cell formation. Codon optimization yielded an ideal CAI value of 0.952 and a GC content of 57.15%, confirming high potential for recombinant expression. Conclusions: This study presents a novel multi-epitope vaccine candidate against reovirus, designed to elicit broad cellular and humoral immunity. Comprehensive in silico analyses confirm its structural stability, potent interaction with innate immune receptors, and high potential for expression. These findings provide a strong rationale for further wet-lab studies to validate its efficacy and advance it as a promising prophylactic candidate. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery, 2nd Edition)
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31 pages, 8743 KB  
Article
Repurposing Cofilin-Targeting Compounds for Ischemic Stroke Through Cheminformatics and Network Pharmacology
by Saleh I. Alaqel, Abida Khan, Mashael N. Alanazi, Naira Nayeem, Hayet Ben Khaled and Mohd Imran
Pharmaceuticals 2025, 18(9), 1323; https://doi.org/10.3390/ph18091323 - 4 Sep 2025
Viewed by 966
Abstract
Background/Objectives: Cofilin, a key regulator of actin cytoskeleton dynamics, contributes to neuroinflammation, synaptic damage, and blood–brain barrier disruption in ischemic stroke. Despite its established role in stroke pathology, cofilin remains largely untargeted by existing therapeutics. This study aimed to identify potential cofilin-binding [...] Read more.
Background/Objectives: Cofilin, a key regulator of actin cytoskeleton dynamics, contributes to neuroinflammation, synaptic damage, and blood–brain barrier disruption in ischemic stroke. Despite its established role in stroke pathology, cofilin remains largely untargeted by existing therapeutics. This study aimed to identify potential cofilin-binding molecules by repurposing LIMK1 inhibitors through an integrated computational strategy. Methods: A cheminformatics pipeline combined QSAR modeling with four molecular fingerprint sets and multiple machine learning algorithms. The best-performing QSAR model (substructure–Random Forest) achieved R2_train = 0.8747 and R2_test = 0.8078, supporting the reliability of compound prioritization. Feature importance was assessed through SHAP analysis. Top candidates were subjected to molecular docking against cofilin, followed by 300 ns molecular dynamics simulations, MM-GBSA binding energy calculations, principal component analysis (PCA), and dynamic cross-correlation matrix (DCCM) analyses. Network pharmacology identified overlapping targets between selected compounds and stroke-related genes. Results: Three compounds, CHEMBL3613624, ZINC000653853876, and Gandotinib, were prioritized based on QSAR performance, binding affinity (−6.68, −6.25, and −5.61 Kcal/mol, respectively), and structural relevance. Docking studies confirmed key interactions with Asp98 and His133 on cofilin. Molecular dynamics simulations supported the stability of these interactions, with Gandotinib showing the highest conformational stability, and ZINC000653853876 exhibiting the most favorable energetic profile. Network pharmacology analysis revealed eight intersecting targets, including MAPK1, PRKCB, HDAC1, and serotonin receptors, associated with neuroinflammatory and vascular pathways in strokes. Conclusions: This study presents a rational, integrative repurposing framework for identifying cofilin-targeting compounds with potential therapeutic relevance in ischemic stroke. The selected candidates warrant further experimental validation. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery, 2nd Edition)
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15 pages, 2489 KB  
Article
Leveraging Natural Compounds for Pancreatic Lipase Inhibition via Virtual Screening
by Emanuele Liborio Citriniti, Roberta Rocca, Claudia Sciacca, Nunzio Cardullo, Vera Muccilli, Francesco Ortuso and Stefano Alcaro
Pharmaceuticals 2025, 18(9), 1246; https://doi.org/10.3390/ph18091246 - 22 Aug 2025
Cited by 1 | Viewed by 1713
Abstract
Background: Pancreatic lipase (PL), the principal enzyme catalyzing the hydrolysis of dietary triacylglycerols in the intestinal lumen, is pivotal for efficient lipid absorption and plays a central role in metabolic homeostasis. Enhanced PL activity promotes excessive lipid assimilation and contributes to positive [...] Read more.
Background: Pancreatic lipase (PL), the principal enzyme catalyzing the hydrolysis of dietary triacylglycerols in the intestinal lumen, is pivotal for efficient lipid absorption and plays a central role in metabolic homeostasis. Enhanced PL activity promotes excessive lipid assimilation and contributes to positive energy balance, key pathophysiological mechanisms underlying the escalating global prevalence of obesity—a complex, multifactorial condition strongly associated with metabolic disorders, including type 2 diabetes mellitus and cardiovascular disease. Inhibition of pancreatic lipase (PL) constitutes a well-established therapeutic approach for attenuating dietary lipid absorption and mitigating obesity. Methods: With the aim to identify putative PL inhibitors, a Structure-Based Virtual Screening (SBVS) of PhytoHub database naturally occurring derivatives was performed. A refined library of 10,404 phytochemicals was virtually screened against a crystal structure of pancreatic lipase. Candidates were filtered out based on binding affinity, Lipinski’s Rule of Five, and structural clustering, resulting in six lead compounds. Results: In vitro, enzymatic assays confirmed theoretical suggestions, highlighting Pinoresinol as the best PL inhibitor. Molecular dynamics simulations, performed to investigate the stability of protein–ligand complexes, revealed key interactions, such as persistent hydrogen bonding to catalytic residues. Conclusions: This integrative computational–experimental workflow highlighted new promising natural PL inhibitors, laying the foundation for future development of safe, plant-derived anti-obesity therapeutics. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery, 2nd Edition)
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Review

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22 pages, 22951 KB  
Review
Advancing PROTAC Discovery Through Artificial Intelligence: Opportunities, Challenges, and Future Directions
by Kwang-Su Park and Minji Jeon
Pharmaceuticals 2025, 18(12), 1793; https://doi.org/10.3390/ph18121793 - 25 Nov 2025
Viewed by 837
Abstract
Proteolysis Targeting Chimeras (PROTACs) represent a transformative modality in drug discovery, enabling the selective degradation of disease-relevant proteins through the ubiquitin proteasome system. Despite their therapeutic promise, the rational design of PROTACs remains a complex and resource-intensive process, involving multiple parameters such as [...] Read more.
Proteolysis Targeting Chimeras (PROTACs) represent a transformative modality in drug discovery, enabling the selective degradation of disease-relevant proteins through the ubiquitin proteasome system. Despite their therapeutic promise, the rational design of PROTACs remains a complex and resource-intensive process, involving multiple parameters such as target and ligase compatibility, ternary complex formation, linker optimization, and degradation efficiency. Recent advances in artificial intelligence (AI) have provided new strategies to address these obstacles, ranging from structure-based modeling of ternary complexes to degradability prediction, generative linker design, and pharmacokinetic property estimation. This review aims to explore how AI can be leveraged directly or indirectly in the PROTAC development pipeline. First, we analyze existing applications of AI, such as ternary complex structure prediction, degradability prediction, linker design, and ADME prediction. We further discuss how other approaches from the related fields may be adapted to address the challenges of PROTAC discovery. Lastly, we discuss challenges that current AI models face, such as limited data, poor interpretability, and low generalizability. Taken together, overcoming these barriers will enable AI-driven strategies to accelerate PROTAC discovery and provide a more rational framework for targeted protein degrader development. Full article
(This article belongs to the Special Issue Computer-Aided Drug Design and Drug Discovery, 2nd Edition)
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