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Pharmaceuticals
Special Issues
Emerging Computational Approaches in Drug Discovery and Design
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Emerging Computational Approaches in Drug Discovery and Design

A Special Issue of Pharmaceuticals (ISSN 1424-8247) belonging to the section "Medicinal Chemistry".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1594

Editor

Dr. Cleber C. Melo-Filho

E-Mail Website
Guest Editor
UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Interests: computer-aided drug design; molecular modeling; cheminformatics; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Computational approaches have been utilized in drug discovery and design for many decades. Given the vast amount of data generated in recent years and advancements in computational power, the field of computer-aided drug design (CADD) has evolved through efforts to integrate well-established methods with innovative techniques. In contrast, adhering to best practices in CADD remains vital, and thoroughly validating any approach is essential.

In this Special Issue, we emphasize the development and application of computational approaches for drug discovery. Approaches and methods include structure- and ligand-based drug design, virtual screening, the quantitative structure–activity/property relationship (QSAR/QSPR), and machine learning. Reviews and research articles on novel method development or the use of established CADD methods are encouraged. We suggest that research manuscripts include a proper explanation of data curation before modeling. Furthermore, the reproducibility of results is highly valued (for example, exchanging codes and data, if applicable). Articles integrating computational approaches with experimental validation are also encouraged.

Dr. Cleber C. Melo-Filho
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-anonymized 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

  • computer-aided drug design (CADD)
  • quantitative structure-activity/property relationship (QSAR/QSPR)
  • cheminformatics
  • virtual screening
  • data curation
  • in silico
  • molecular docking
  • machine learning
  • deep learning

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Published Papers (2 papers)

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18 pages, 1547 KB  
Article
Computational Prediction of the Severity of Adverse Drug Reactions Caused by Drug–Drug Interactions
by Vladislav S. Sukhachev, Sergey M. Ivanov, Dmitry A. Filimonov, Anastasia V. Rudik and Vladimir V. Poroikov
Pharmaceuticals 2026, 19(9), 1337; https://doi.org/10.3390/ph19091337 - 24 Aug 2026
Viewed by 350
Abstract
Background/Objectives: Adverse drug reactions (ADRs) caused by drug–drug interactions (DDIs) represent an important problem in pharmacotherapy, especially in patients receiving multiple medications. Most computational approaches to DDI-associated ADR prediction formulate the task as a binary classification, but they do not explicitly consider [...] Read more.
Background/Objectives: Adverse drug reactions (ADRs) caused by drug–drug interactions (DDIs) represent an important problem in pharmacotherapy, especially in patients receiving multiple medications. Most computational approaches to DDI-associated ADR prediction formulate the task as a binary classification, but they do not explicitly consider the severity of adverse reactions. Our study aims to develop structure-based models that predict the severity of ADRs associated with specific drug pairs. Methods: Datasets were generated using DrugMAP as the source of drug pair–ADR associations with annotated severity categories, and TwoSides was used as an additional source to generate conditionally negative examples. The drug pairs were represented using PoSMNA descriptors, which encode pair-specific structural features derived from the molecular structures of both compounds. Predictive models were built using PASS DDI software. Model performance was evaluated using a modified cross-validation procedure that excluded compound-level overlap between the training and test sets, thereby reducing information leakage caused by the repeated occurrence of the same drugs in different pairs. Results: Models were developed for 14 clinically relevant ADR types, including cardiovascular, hepatotoxic, nephrotoxic, hemorrhagic, metabolic, and neurological effects. The unweighted class-level macro-average AUC values ranged from 0.830 for the Major category to 0.911 for the Minor category, while balanced accuracy ranged from 0.776 to 0.857. Predictive performance varied significantly between ADR types and severity categories. Higher accuracy was observed for some ADR types that were better captured by the structure-based descriptors used in this study, whereas complex multifactorial reactions, such as hepatotoxicity, were less accurately predicted. Case-based assessment using clinically documented drug combinations showed that the predicted severity profiles were generally consistent with the expected clinical risk patterns. Conclusions: The proposed approach demonstrates that the PoSMNA descriptors of drug pairs can be used for preliminary prediction of DDI-associated ADR severity. The developed models can help to filter out potentially dangerous drug combinations at an early stage and are implemented in the AdverDDIPred web-application. Full article
(This article belongs to the Special Issue Emerging Computational Approaches in Drug Discovery and Design)
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17 pages, 10791 KB  
Article
An Experimentally Validated Structure-Based Virtual Screening Approach to Identify Nucleotide-Binding Protein Inhibitors as a New Source of Kinase Inhibitors
by Nicolas Bosc, Fabrice Carles, Jade Fogha, Blandine Baratte, Stéphane Bach, Samia Aci-Sèche, Stéphane Bourg, Sylvain Routier, Sandrine Ruchaud, Frédéric Buron and Pascal Bonnet
Pharmaceuticals 2026, 19(8), 1138; https://doi.org/10.3390/ph19081138 - 23 Jul 2026
Viewed by 351
Abstract
Background/Objectives: Protein kinases represent major therapeutic targets because dysregulation of their phosphorylation activity is associated with several diseases, including cancer, diabetes, and inflammatory disorders. Thus, many researchers in the pharmaceutical filed are making significant effort to design potent new protein kinase inhibitors (PKIs) [...] Read more.
Background/Objectives: Protein kinases represent major therapeutic targets because dysregulation of their phosphorylation activity is associated with several diseases, including cancer, diabetes, and inflammatory disorders. Thus, many researchers in the pharmaceutical filed are making significant effort to design potent new protein kinase inhibitors (PKIs) as potential drugs. In this context, we aimed to identify new alternatives by exploiting the chemical space defined by ligands of the nucleotide-binding protein family for the discovery of novel protein kinase inhibitors. Protein kinases bind the nucleotide adenosine triphosphate (ATP) and belong to the nucleotide-binding protein group. Methods: All ligands of the nucleotide-binding protein family, excluding known kinase inhibitors, that were identified in the ChEMBL database were used in a structure-based virtual screening approach. From this set, we aimed to identify novel nucleotide-binding protein inhibitors (NBPIs) as novel kinase inhibitors. A total of 19,709 NBPI compounds that were dissimilar to known PKIs were docked on five protein kinases, and the 200 best scoring docking poses were retained for potential purchase. Results: Only 25 compounds were commercially available in stock and were evaluated experimentally on a panel of 10 diverse protein kinases. Three NBPI compounds, one of which had originally been identified as active against the ATP-binding cassette transporter ABCG2, were identified as Haspin kinase inhibitors with micromolar activity. Conclusions: This study presents an efficient computational approach to identifying novel kinase inhibitors from a database of ligands of the nucleotide-binding protein family, and the protocol could be applied to other protein target families. Full article
(This article belongs to the Special Issue Emerging Computational Approaches in Drug Discovery and Design)
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