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Advances in Structural Bioinformatics and Next-Generation Sequence Analysis for Drug...
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Advances in Structural Bioinformatics and Next-Generation Sequence Analysis for Drug Design

A special issue of BioMedInformatics (ISSN 2673-7426).

Deadline for manuscript submissions: closed (31 December 2025) | Viewed by 24170

Special Issue Editor

Dr. Alexandre G. De Brevern

E-Mail Website
Guest Editor
Department of Biological Research on the Red Blood Cells, INTS, INSERM UMR_S 1134, Université de Paris, Université de la Réunion, 75739 Paris, France
Interests: structural bioinformatics; bioinformatics; next-generation sequence; drug design; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Structural Bioinformatics and Next-Generation Sequencing (NGS) are two emerging fields that have revolutionized the way we approach drug design. Structural Bioinformatics allows us to analyze the three-dimensional structures of proteins and other biological macromolecules and to gain insights into their functions and interactions. NGS has enabled us to sequence large amounts of genetic data quickly and efficiently and to explore the genetic basis of diseases and drug responses.

This Special Issue aims to bring together researchers and practitioners working in the areas of Structural Bioinformatics, Bioinformatics, Next-Generation Sequencing, Drug Design, and Deep Learning. We welcome original research articles that report on novel and significant findings in these areas.

Scope and Topics:

We invite high-quality research papers that report on innovative and significant research findings in the following areas:

    Structural Bioinformatics for Drug Design
    Computational methods for analyzing protein–ligand interactions;
    Next-Generation Sequencing for Personalized Medicine;
    Genomic data analysis for drug discovery;
    Deep learning in structural biology and drug design;

Submission Guidelines:

We welcome original and unpublished research articles that report on innovative and significant research findings in the field of Structural Bioinformatics and Next-Generation Sequencing for Drug Design. The submissions should not have been published elsewhere and should not be under consideration for publication in any other venue. We only accept full-length research articles for this Special Issue, and we encourage authors to follow the standard research paper format and provide a clear and concise description of their research findings.

All submitted papers will be peer-reviewed by experts in the field. Manuscripts should be submitted in English, and the submission should adhere to the journal's guidelines and formatting requirements.

Conclusion:

This Special Issue aims to present cutting-edge research in the field of Structural Bioinformatics and Next-Generation Sequencing for Drug Design. We welcome high-quality research papers that report on novel and significant research findings in these areas. We look forward to receiving your submissions and making this Special Issue a success.

Prof. Dr. Alexandre G. De Brevern
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. BioMedInformatics 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 1200 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

  • structural Bioinformatics for drug design
  • computational methods for analyzing protein–ligand interactions
  • next-generation sequencing for personalized medicine
  • genomic data analysis for drug discovery
  • deep learning in structural biology and drug design

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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14 pages, 1277 KB  
Article
Comparative Analysis of Skin Microbiome in Acne Lesions and Healthy Skin Using 16S rRNA Gene Sequencing
by Fadilah Fadilah, Hartanti Dian Ikawati, Anis Karuniawati, Linda Erlina, Fitria Agustina, Rafika Indah Paramita and Mohd Azrul Naim Mohamad
BioMedInformatics 2026, 6(1), 1; https://doi.org/10.3390/biomedinformatics6010001 - 23 Dec 2025
Viewed by 1573
Abstract
Acne vulgaris (AV) is a common dermatological disorder in adolescents, encompassing both non-inflammatory and inflammatory lesions, with growing evidence implicating the skin microbiome in its pathogenesis. This study analyzed skin lesion samples from 12 adolescents with AV using 16S rRNA high-throughput sequencing, with [...] Read more.
Acne vulgaris (AV) is a common dermatological disorder in adolescents, encompassing both non-inflammatory and inflammatory lesions, with growing evidence implicating the skin microbiome in its pathogenesis. This study analyzed skin lesion samples from 12 adolescents with AV using 16S rRNA high-throughput sequencing, with 12 healthy skin microbiome datasets as references. A total of 4.7 million high-quality reads were obtained, yielding 765,211 clean reads clustered into 1013 operational taxonomic units (OTUs). Microbial communities in lesions differed markedly from those in healthy skin. At the phylum level, lesions showed higher proportions of Bacteroidota and Bacillota, whereas healthy skin was dominated by Actinobacteria. At the genus level, lesions were modestly but significantly higher in Staphylococcus, Corynebacterium, and Peptoniphilus, while Cutibacterium was more abundant in healthy skin. Alpha diversity analysis revealed greater species richness and phylogenetic diversity in healthy skin, but higher evenness in lesions. Beta diversity confirmed significant differences in community structure. Functional prediction identified 391 metabolic pathways, 163 of which differed significantly; only three were enriched in lesions, while 160 were more abundant in healthy skin. Lipase activity was elevated in lesions, whereas hyaluronate lyase activity was higher in healthy skin. These findings indicate that healthy skin supports a richer and more functionally diverse microbial metabolism, whereas acne lesions are associated with reduced metabolic capabilities. Overall, the acne lesion microbiome exhibits reduced diversity, altered bacterial composition, and distinct functional traits compared to healthy skin, underscoring the role of microbial imbalance in acne and suggesting potential microbial targets for treatment. Full article
(This article belongs to the Special Issue Advances in Structural Bioinformatics and Next-Generation Sequence Analysis for Drug Design)
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Review

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16 pages, 750 KB  
Review
Role of Artificial Neural Networks in Optimizing Bioconversion of Antiretroviral Drugs: A Review
by Nelson T. Tsotetsi, Ndiwanga F. Rasifudi, Beauty Magage and Lukhanyo Mekuto
BioMedInformatics 2026, 6(3), 30; https://doi.org/10.3390/biomedinformatics6030030 - 15 May 2026
Viewed by 198
Abstract
Antiretroviral drugs (ARVDs) remain the cornerstone of HIV/AIDS management, but their therapeutic efficacy and safety are highly influenced by bioconversion processes such as hepatic metabolism and enzymatic transformation. Variability in metabolic pathways, mediated by cytochrome P450 enzymes and other liver-based systems, contributes to [...] Read more.
Antiretroviral drugs (ARVDs) remain the cornerstone of HIV/AIDS management, but their therapeutic efficacy and safety are highly influenced by bioconversion processes such as hepatic metabolism and enzymatic transformation. Variability in metabolic pathways, mediated by cytochrome P450 enzymes and other liver-based systems, contributes to interindividual differences in drug response, toxicity, and resistance. Recent advances in artificial intelligence, particularly artificial neural networks (ANNs), offer promising tools for modeling and optimizing these complex bioconversion processes. ANNs are capable of learning nonlinear relationships from high-dimensional datasets, making them ideal for predicting the pharmacokinetic parameters, enzyme–substrate interactions, and metabolic stability of ARVDs. This review explores the emerging role of ANNs in understanding and optimizing the metabolic transformation of antiretroviral agents. Key applications are discussed, including prediction of drug–enzyme interactions, in silico modeling of hepatic clearance, and simulation of enzyme kinetics. The integration of molecular descriptors, omics data, and clinical parameters into ANN models allows for improved prediction accuracy and personalized therapy. Furthermore, ANN-based tools can aid in early-stage drug development by identifying metabolic liabilities and guiding structural modifications to enhance metabolic stability. Despite their potential, challenges such as data scarcity, model interpretability, and standardization remain. Future research should focus on hybrid models combining ANN with mechanistic pharmacokinetics, the incorporation of real-world patient data, and validation against experimental outcomes. Overall, ANNs represent a powerful approach to optimizing ARVDs bioconversion, with the potential to improve efficacy, reduce toxicity, and support the development of next-generation antiretroviral therapies Full article
(This article belongs to the Special Issue Advances in Structural Bioinformatics and Next-Generation Sequence Analysis for Drug Design)
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21 pages, 831 KB  
Review
Computational Strategies to Enhance Cell-Free Protein Synthesis Efficiency
by Iyappan Kathirvel and Neela Gayathri Ganesan
BioMedInformatics 2024, 4(3), 2022-2042; https://doi.org/10.3390/biomedinformatics4030110 - 10 Sep 2024
Cited by 10 | Viewed by 6495
Abstract
Cell-free protein synthesis (CFPS) has emerged as a powerful tool for protein production, with applications ranging from basic research to biotechnology and pharmaceutical development. However, enhancing the efficiency of CFPS systems remains a crucial challenge for realizing their full potential. Computational strategies offer [...] Read more.
Cell-free protein synthesis (CFPS) has emerged as a powerful tool for protein production, with applications ranging from basic research to biotechnology and pharmaceutical development. However, enhancing the efficiency of CFPS systems remains a crucial challenge for realizing their full potential. Computational strategies offer promising avenues for optimizing CFPS efficiency by providing insights into complex biological processes and enabling rational design approaches. This review provides a comprehensive overview of the computational approaches aimed at enhancing CFPS efficiency. The introduction outlines the significance of CFPS and the role of computational methods in addressing efficiency limitations. It discusses mathematical modeling and simulation-based approaches for predicting protein synthesis kinetics and optimizing CFPS reactions. The review also delves into the design of DNA templates, including codon optimization strategies and mRNA secondary structure prediction tools, to improve protein synthesis efficiency. Furthermore, it explores computational techniques for engineering cell-free transcription and translation machinery, such as the rational design of expression systems and the predictive modeling of ribosome dynamics. The predictive modeling of metabolic pathways and the energy utilization in CFPS systems is also discussed, highlighting metabolic flux analysis and resource allocation strategies. Machine learning and artificial intelligence approaches are being increasingly employed for CFPS optimization, including neural network models, deep learning algorithms, and reinforcement learning for adaptive control. This review presents case studies showcasing successful CFPS optimization using computational methods and discusses applications in synthetic biology, biotechnology, and pharmaceuticals. The challenges and limitations of current computational approaches are addressed, along with future perspectives and emerging trends, such as the integration of multi-omics data and advances in high-throughput screening. The conclusion summarizes key findings, discusses implications for future research directions and applications, and emphasizes opportunities for interdisciplinary collaboration. This review offers valuable insights and prospects regarding computational strategies to enhance CFPS efficiency. It serves as a comprehensive resource, consolidating current knowledge in the field and guiding further advancements. Full article
(This article belongs to the Special Issue Advances in Structural Bioinformatics and Next-Generation Sequence Analysis for Drug Design)
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16 pages, 1545 KB  
Review
Unlocking the Future of Drug Development: Generative AI, Digital Twins, and Beyond
by Zamara Mariam, Sarfaraz K. Niazi and Matthias Magoola
BioMedInformatics 2024, 4(2), 1441-1456; https://doi.org/10.3390/biomedinformatics4020079 - 6 Jun 2024
Cited by 40 | Viewed by 8713
Abstract
This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and [...] Read more.
This article delves into the intersection of generative AI and digital twins within drug discovery, exploring their synergistic potential to revolutionize pharmaceutical research and development. Through various instances and examples, we illuminate how generative AI algorithms, capable of simulating vast chemical spaces and predicting molecular properties, are increasingly integrated with digital twins of biological systems to expedite drug discovery. By harnessing the power of computational models and machine learning, researchers can design novel compounds tailored to specific targets, optimize drug candidates, and simulate their behavior within virtual biological environments. This paradigm shift offers unprecedented opportunities for accelerating drug development, reducing costs, and, ultimately, improving patient outcomes. As we navigate this rapidly evolving landscape, collaboration between interdisciplinary teams and continued innovation will be paramount in realizing the promise of generative AI and digital twins in advancing drug discovery. Full article
(This article belongs to the Special Issue Advances in Structural Bioinformatics and Next-Generation Sequence Analysis for Drug Design)
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19 pages, 2539 KB  
Review
Transforming Drug Design: Innovations in Computer-Aided Discovery for Biosimilar Agents
by Shadi Askari, Alireza Ghofrani and Hamed Taherdoost
BioMedInformatics 2023, 3(4), 1178-1196; https://doi.org/10.3390/biomedinformatics3040070 - 8 Dec 2023
Cited by 15 | Viewed by 5059
Abstract
In pharmaceutical research and development, pursuing novel therapeutics and optimizing existing drugs have been revolutionized by the fusion of cutting-edge technologies and computational methodologies. Over the past few decades, the field of drug design has undergone a remarkable transformation, catalyzed by the rapid [...] Read more.
In pharmaceutical research and development, pursuing novel therapeutics and optimizing existing drugs have been revolutionized by the fusion of cutting-edge technologies and computational methodologies. Over the past few decades, the field of drug design has undergone a remarkable transformation, catalyzed by the rapid advancement of computer-aided discovery techniques and the emergence of biosimilar agents. This dynamic interplay between scientific innovation and technological prowess has expedited the drug discovery process and paved the way for more targeted, effective, and personalized treatment approaches. This review investigates the transformative computer-aided discovery techniques for biosimilar agents in reshaping drug design. It examines how computational methods expedite drug candidate identification and explores the rise of cost-effective biosimilars as alternatives to biologics. Through this analysis, this study highlights the potential of these innovations to enhance the efficiency and accessibility of pharmaceutical development. It represents a pioneering effort to examine how computer-aided discovery is revolutionizing biosimilar agent development, exploring its applications, challenges, and prospects. Full article
(This article belongs to the Special Issue Advances in Structural Bioinformatics and Next-Generation Sequence Analysis for Drug Design)
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