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Pharmaceuticals
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Computational Methods in Drug Development
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Computational Methods in Drug Development

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

Deadline for manuscript submissions: 26 May 2026 | Viewed by 5533

Special Issue Editor

Prof. Dr. Yunjie Zhao

E-Mail Website
Guest Editor
Department of Physics, Institute of Biophysics, Central China Normal University, Wuhan 430079, China
Interests: structure prediction; regulation mechanisms; drug design
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Advancements in computational methods have significantly changed the drug development process, offering new solutions to speed up the discovery and improvement in therapeutic agents. These methods cover various technologies, from molecular modeling to artificial intelligence, and contribute to different stages of the process.

Recently, computational methods have significantly increased in areas such as predicting the structures of proteins, RNA, drug molecules, and their complexes, identifying new drug targets, and optimizing drug–receptor interactions. Experimental methods for uncovering molecular structures are often expensive and time-consuming, making computational methods efficient alternatives in these cases. They also help understand complex biological systems, enabling screening large chemical libraries and reducing the time and cost associated with traditional experimental approaches.

This Special Issue aims to gather the latest research and reviews on computational strategies in drug development. We encourage submissions that demonstrate innovative computational techniques, including computer-aided drug design, molecular docking and scoring, virtual screening, and the application of various machine learning approaches in drug discovery. By highlighting these advances, we aim to show the transformative potential of computational methods in developing new and efficacious therapeutics.

We welcome submissions discussing the challenges and opportunities in this rapidly evolving field, particularly interdisciplinary studies that combine computational methods with experimental validation. This Special Issue will provide an up-to-date overview of computational drug development and set the stage for future research directions.

Dr. Yunjie Zhao
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. 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

  • computational modeling
  • DNA/RNA and protein
  • drug discovery and design
  • systems biology
  • channels and membranes
  • biomedical data analysis
  • artificial intelligence
  • molecule docking and scoring

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Published Papers (6 papers)

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Research

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29 pages, 8935 KiB  
Article
Resveratrol Alleviates Inflammatory Response Through P2X7/NLRP3 Signaling Pathway: In Silico and In Vitro Evidence from Activated Microglia
by Bianca Fagan Bissacotti, Marcylene Vieira da Silveira, Charles Elias Assmann, Priscila Marquezan Copetti, André Flores dos Santos, Solange Binotto Fagan, João Augusto Pereira da Rocha, Maria Rosa Chitolina Schetinger, Vera Maria Melchiors Morsch, Nathieli Bianchin Bottari, Alencar Kolinski Machado and Aleksandro Schafer da Silva
Pharmaceuticals 2025, 18(7), 950; https://doi.org/10.3390/ph18070950 - 24 Jun 2025
Viewed by 225
Abstract
Background/Objectives: Chronic inflammation and inappropriate NLRP3 inflammasome regulation are related to many brain diseases. Purinergic mediators may play an important role in inflammation regulation and could be targeted for effective therapies for these illnesses. We evaluated resveratrol’s anti-neuroinflammatory potential in BV-2 microglia [...] Read more.
Background/Objectives: Chronic inflammation and inappropriate NLRP3 inflammasome regulation are related to many brain diseases. Purinergic mediators may play an important role in inflammation regulation and could be targeted for effective therapies for these illnesses. We evaluated resveratrol’s anti-neuroinflammatory potential in BV-2 microglia cells using an innovative in vitro method of NLRP3 inflammasome activation, correlating with the P2X7 purinergic receptor. Methods: In silico analyses were used to estimate resveratrol’s interaction with NLRP3, and its cytotoxicity was measured for 24, 48, and 72 h. Moreover, microglia were exposed to lipopolysaccharide and nigericin to activate the NLRP3 inflammasome and treated with resveratrol between these inflammatory agents. Results: It was found that resveratrol has binding compatible with modulating NLRP3. Specifically, 0.1–25 µM of resveratrol presented a favorable safety profile in BV-2 cells. Microglia exposed to the inflammatory agents had increased levels of oxidative species, the P2X7 receptor, and pro-inflammatory cytokines. However, resveratrol decreased the NLRP3, caspase-1, IL-1β, IL-6, and TNF-α mRNA levels and protein density; on the other hand, IL-10 was increased, acting as a protector, preventing exacerbated inflammation. Under resveratrol exposure, P2X7 was negatively expressed, regulating inflammation to establish homeostasis and microglial proliferation. Additionally, resveratrol activates the A1 adenosine receptor, possibly correlated with neuroprotective effects. Conclusions: We confirmed the anti-neuroinflammatory action of resveratrol via the P2X7 receptor and NLRP3’s combined modulation, regulating the cell cycle and reducing pro-inflammatory and oxidant agents. Considering this pathway, resveratrol could be a candidate for further investigations as a potential treatment against neuroinflammatory diseases. Full article
(This article belongs to the Special Issue Computational Methods in Drug Development)
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22 pages, 4691 KiB  
Article
Exploring Brazilian Green Propolis Phytochemicals in the Search for Potential Inhibitors of B-Raf600E Enzyme: A Theoretical Approach
by Garcia Ferreira de Souza, Airis Farias Santana, Fernanda Sanches Kuhl Antunes, Ramon Martins Cogo, Matheus Dornellas Pereira, Daniela Gonçales Galasse Rando and Carolina Passarelli Gonçalves
Pharmaceuticals 2025, 18(6), 902; https://doi.org/10.3390/ph18060902 - 16 Jun 2025
Viewed by 267
Abstract
Background/Objectives: Melanoma is one of the most aggressive forms of skin cancer and is frequently associated with the B-Raf600E mutation, which constitutively activates the MAPK signaling pathway. Although selective inhibitors such as Vemurafenib offer clinical benefits, their long-term efficacy is often [...] Read more.
Background/Objectives: Melanoma is one of the most aggressive forms of skin cancer and is frequently associated with the B-Raf600E mutation, which constitutively activates the MAPK signaling pathway. Although selective inhibitors such as Vemurafenib offer clinical benefits, their long-term efficacy is often hindered by resistance mechanisms and adverse effects. In this study, twelve phytochemicals from Brazilian green propolis were evaluated for their potential as selective B-Raf600E inhibitors using a computational approach. Methods: Physicochemical, ADME, and electronic properties were assessed, followed by molecular docking using the B-Raf600E crystal structure (PDB ID: 3OG7). Redocking validation and 500 ns molecular dynamics simulations were performed to investigate the stability of the ligand-protein complexes, and free energy calculations were then computed. Results: Among the tested compounds, Artepillin C exhibited the strongest binding affinity (−8.17 kcal/mol) in docking and maintained stable interactions with key catalytic residues throughout the simulation, also presenting free energy of binding ΔG of −20.77 kcal/mol. HOMO-LUMO and electrostatic potential analyses further supported its reactivity and selectivity. Notably, Artepillin C remained bound within the ATP-binding site, mimicking several critical interactions observed with Vemurafenib. Results: Among the tested compounds, Artepillin C exhibited the strongest binding affinity (−8.17 kcal/mol) and maintained stable interactions with key catalytic residues throughout the simulation. HOMO-LUMO and electrostatic potential analyses further supported its reactivity and selectivity. Notably, Artepillin C remained bound within the ATP-binding site, mimicking several critical interactions observed with Vemurafenib. Conclusions: These findings indicate that Artepillin C is a promising natural compound for further development as a selective B-Raf600E inhibitor and suggest its potential utility in melanoma treatment strategies. This study reinforces the value of natural products as scaffolds for targeted drug design and supports continued experimental validation. Full article
(This article belongs to the Special Issue Computational Methods in Drug Development)
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15 pages, 717 KiB  
Article
Pharmacokinetic Equations Applied to Obtain New Topological Models in the Search of Antibacterial Compounds
by Jose I. Bueso-Bordils, Gerardo M. Antón-Fos, Rafael Martín-Algarra and Pedro A. Alemán-López
Pharmaceuticals 2025, 18(6), 865; https://doi.org/10.3390/ph18060865 - 10 Jun 2025
Viewed by 349
Abstract
Background: QSAR (Quantitative Structure–Activity Relationships) methods have been the basis for the design of new molecules with a certain activity. The great advantage of QSAR methods is that they can predict the pharmacological activity of compounds without the need to obtain or synthesize [...] Read more.
Background: QSAR (Quantitative Structure–Activity Relationships) methods have been the basis for the design of new molecules with a certain activity. The great advantage of QSAR methods is that they can predict the pharmacological activity of compounds without the need to obtain or synthesize them previously. Currently, the development of antibiotic resistance by microorganisms is the most important issue in the treatment of infectious diseases. This elevated resistance is associated with expanded morbidity and mortality, as well as an increase in healthcare costs. The development of new molecules with antibacterial activity is therefore urgently needed. Methods: By means of molecular topology, we developed discriminant functions (DF1 and DF2) capable of predicting antibacterial activity. When applied to a database with 6373 chemicals, they selected 266 molecules as candidates, from which 41% have this activity, according to the bibliography. Regression equations determining pharmacokinetic properties such as mean residence time (MRT), volume of distribution (VD), and clearance (CL) were applied to the selected molecules. Results: We have observed that most antibacterial compounds have pharmacokinetic theoretical values in the intervals 20 > MRT > 0, 3 > VD > 0, and 500 > CL > 0. We have applied these intervals to our antibacterial model with the objective of finding new antibacterials with a good pharmacokinetic profile. We show that they are an effective tool for discriminating antibacterial compounds, increasing the bibliographic success rate to 50.8, 59, and 61.5%, respectively. When drug-like filters are applied to these new models, the vast majority (89.9–100%) of the selected molecules present antibacterial activity. Conclusions: Considering these results, these new models could avoid the application of drug-likeness filters when searching for new potential antibacterials. All of this proves the usefulness of these mathematical–topological models. Full article
(This article belongs to the Special Issue Computational Methods in Drug Development)
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26 pages, 16481 KiB  
Article
Systems Biology-Driven Discovery of Host-Targeted Therapeutics for Oropouche Virus: Integrating Network Pharmacology, Molecular Docking, and Drug Repurposing
by Pranab Dev Sharma, Abdulrahman Mohammed Alhudhaibi, Abdullah Al Noman, Emad M. Abdallah, Tarek H. Taha and Himanshu Sharma
Pharmaceuticals 2025, 18(5), 613; https://doi.org/10.3390/ph18050613 - 23 Apr 2025
Viewed by 911
Abstract
Background: Oropouche virus (OROV), part of the Peribunyaviridae family, is an emerging pathogen causing Oropouche fever, a febrile illness endemic in South and Central America. Transmitted primarily through midge bites (Culicoides paraensis), OROV has no specific antiviral treatment or vaccine. This [...] Read more.
Background: Oropouche virus (OROV), part of the Peribunyaviridae family, is an emerging pathogen causing Oropouche fever, a febrile illness endemic in South and Central America. Transmitted primarily through midge bites (Culicoides paraensis), OROV has no specific antiviral treatment or vaccine. This study aims to identify host-targeted therapeutics against OROV using computational approaches, offering a potential strategy for sustainable antiviral drug discovery. Methods: Virus-associated host targets were identified using the OMIM and GeneCards databases. The Enrichr and DSigDB platforms were used for drug prediction, filtering compounds based on Lipinski’s rule for drug likeness. A protein–protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape 3.10.3 software. Four key host targets—IL10, FASLG, PTPRC, and FCGR3A—were prioritized based on their roles in immune modulation and OROV pathogenesis. Molecular docking simulations were performed using the PyRx software to evaluate the binding affinities of selected small-molecule inhibitors—Acetohexamide, Deptropine, Methotrexate, Retinoic Acid, and 3-Azido-3-deoxythymidine—against the identified targets. Results: The PPI network analysis highlighted immune-mediated pathways such as Fc-gamma receptor signaling, cytokine control, and T-cell receptor signaling as critical intervention points. Molecular docking revealed strong binding affinities between the selected compounds and the prioritized targets, suggesting their potential efficacy as host-targeting antiviral candidates. Acetohexamide and Deptropine showed strong binding to multiple targets, indicating broad-spectrum antiviral potential. Further in vitro and in vivo validations are needed to confirm these findings and translate them into clinically relevant treatments. Conclusions: This study highlights the potential of using computational approaches to identify host-targeted therapeutics for Oropouche virus (OROV). By targeting key host proteins involved in immune modulation—IL10, FASLG, PTPRC, and FCGR3A—the selected compounds, Acetohexamide and Deptropine, demonstrate strong binding affinities, suggesting their potential as broad-spectrum antiviral candidates. Further experimental validation is needed to confirm their efficacy and potential for clinical application, offering a promising strategy for sustainable antiviral drug discovery. Full article
(This article belongs to the Special Issue Computational Methods in Drug Development)
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31 pages, 9684 KiB  
Article
Design, Synthesis, and Computational Evaluation of 3,4-Dihydroquinolin-2(1H)-One Analogues as Potential VEGFR2 Inhibitors in Glioblastoma Multiforme
by Shafeek Buhlak, Nadeem Abad, Jihane Akachar, Sana Saffour, Yunus Kesgun, Sevval Dik, Betul Yasin, Gizem Bati-Ayaz, Essam Hanashalshahaby, Hasan Türkez and Adil Mardinoglu
Pharmaceuticals 2025, 18(2), 233; https://doi.org/10.3390/ph18020233 - 8 Feb 2025
Viewed by 1177
Abstract
Background/Objectives: Glioblastoma multiforme (GBM), an aggressive and deadly brain tumour, presents significant challenges in achieving effective treatment due to its resistance to current therapies and poor prognosis. This study aimed to synthesise and evaluate 23 novel analogues of 3,4-dihydroquinolin-2(1H)-one, designed [...] Read more.
Background/Objectives: Glioblastoma multiforme (GBM), an aggressive and deadly brain tumour, presents significant challenges in achieving effective treatment due to its resistance to current therapies and poor prognosis. This study aimed to synthesise and evaluate 23 novel analogues of 3,4-dihydroquinolin-2(1H)-one, designed to enhance druggability and solubility, and to investigate their potential as VEGFR2 inhibitors for GBM treatment. Methods: The synthesised compounds were analysed using in silico methods, including molecular docking and dynamics studies, to assess their interactions with key residues within the VEGFR2 binding pocket. In vitro evaluations were performed on U87-MG and U138-MG GBM cell lines using MTT assays to determine the IC50 values of the compounds. Results: Among the tested compounds, 4u (IC50 = 7.96 μM), 4t (IC50 = 10.48 μM), 4m (IC50 = 4.20 μM), and 4q (IC50 = 8.00 μM) demonstrated significant antiproliferative effects against both the U87-MG and U138-MG cell lines. These compounds exhibited markedly higher efficacy compared to temozolomide (TMZ), which showed IC50 values of 92.90 μM and 93.09 μM for U87-MG and U138-MG, respectively. Molecular docking and dynamics studies confirmed strong interactions between the compounds and VEGFR2 kinase, supporting their substantial anti-cancer activity. Conclusions: This study highlights the promising potential of 3,4-dihydroquinolin-2(1H)-one analogues, particularly 4m, 4q, 4t, and 4u, as VEGFR2-targeting therapeutic agents for GBM treatment. Further detailed research is warranted to validate and expand upon these findings. Full article
(This article belongs to the Special Issue Computational Methods in Drug Development)
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Review

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30 pages, 2081 KiB  
Review
The Potential of Artificial Intelligence in Pharmaceutical Innovation: From Drug Discovery to Clinical Trials
by Vera Malheiro, Beatriz Santos, Ana Figueiras and Filipa Mascarenhas-Melo
Pharmaceuticals 2025, 18(6), 788; https://doi.org/10.3390/ph18060788 - 25 May 2025
Viewed by 1803
Abstract
Artificial intelligence (AI) is a subfield of computer science focused on developing systems that can execute tasks traditionally associated with human intelligence. AI systems work through algorithms based on rules or instructions that enable the machine to make decisions. With the advancement of [...] Read more.
Artificial intelligence (AI) is a subfield of computer science focused on developing systems that can execute tasks traditionally associated with human intelligence. AI systems work through algorithms based on rules or instructions that enable the machine to make decisions. With the advancement of science, more sophisticated AI techniques, such as machine learning and deep learning, have been developed, allowing machines to learn from large amounts of data and improve their performance over time. The pharmaceutical industry has greatly benefited from the development of this technology. AI has revolutionized drug discovery and development by enabling rapid and effective analysis of vast volumes of biological and chemical data during the identification of new therapeutic compounds. The algorithms developed can predict the efficacy, toxicity, and possible adverse effects of new drugs, optimize the steps involved in clinical trials, reduce associated time and costs, and facilitate the implementation of innovative drugs in the market, making it easier to develop precise therapies tailored to the individual genetic profile of patients. Despite significant advancements, there are still gaps in the application of AI, particularly due to the lack of comprehensive regulation. The constant evolution of this technology requires ongoing and in-depth legislative oversight to ensure its use remains safe, ethical, and free from bias. This review explores the role of AI in drug development, assessing its potential to enhance formulation, accelerate discovery, and repurpose existing medications. It highlights AI’s impact across all stages, from initial research to clinical trials, emphasizing its ability to optimize processes, drive innovation, and improve therapeutic outcomes. Full article
(This article belongs to the Special Issue Computational Methods in Drug Development)
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