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Advances in Chemical Research on Biomarkers, Drugs of Abuse, Medicines, and Computational Approaches

A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Medicinal Chemistry".

Deadline for manuscript submissions: 31 January 2026 | Viewed by 468

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Guest Editor
National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Road, Building 5, Room 5C-109A, Jefferson, AR 72079, USA
Interests: pharmacogenomics; personalized medicine; toxicogenomics; bioinformatics; predictive toxicology
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Special Issue Information

Dear Colleagues,

We invite submissions for a Special Issue focused on cutting-edge advancements in chemical research on biomarkers, drugs of abuse, therapeutic agents, and computational approaches (particularly those exploring protein structures). This Special Issue will highlight interdisciplinary methodologies, spanning both experimental and computational research, that contribute to the identification, quantification, and analysis of biomarkers, drugs, and novel medicines.

This Special Issue seeks contributions on innovative bioanalytical methods for detecting disease biomarkers and drugs of abuse, new therapeutic agents, and molecular mechanisms, especially those elucidating protein structure and function. Research that leverages high-resolution analytical techniques (e.g., time-of-flight, Orbitrap, and mass spectrometry), as well as advanced computational methods such as molecular dynamics simulations, protein-ligand docking, and machine learning models, is particularly encouraged. We also welcome submissions focused on novel sample preparation methods, especially those promoting low-sample, low-cost, and low-solvent usage, as well as the analysis of alternative samples.

We invite original research articles, short communications, and reviews, with topics including the following:

  • Experimental and computational methods for biomarker discovery and disease diagnosis;
  • Detection and monitoring of drugs of abuse and novel psychoactive substances;
  • Bioanalytical and computational approaches in drug design, therapeutic monitoring, and pharmacokinetics;
  • Innovations in sample preparation techniques for complex biological matrices;
  • Protein structure and function studies, including the computational modeling of interactions with drugs or biomarkers;
  • Applications of bioanalytical and computational techniques in personalized medicine, toxicology, and pharmaceutical sciences.

This Special Issue will serve as a comprehensive resource for researchers and professionals in chemistry, bioinformatics, pharmacology, biotechnology, and toxicology, providing cross-disciplinary insights and enabling future advancements.

Dr. Huixiao Hong
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. 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

  • biomarker discovery
  • drugs of abuse
  • therapeutic agents
  • protein structure analysis
  • computational drug design
  • bioanalytical methods
  • mass spectrometry
  • molecular dynamics simulations

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Published Papers (2 papers)

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Research

24 pages, 2292 KiB  
Article
Integrating Molecular Dynamics, Molecular Docking, and Machine Learning for Predicting SARS-CoV-2 Papain-like Protease Binders
by Ann Varghese, Jie Liu, Tucker A. Patterson and Huixiao Hong
Molecules 2025, 30(14), 2985; https://doi.org/10.3390/molecules30142985 - 16 Jul 2025
Abstract
Coronavirus disease 2019 (COVID-19) produced devastating health and economic impacts worldwide. While progress has been made in vaccine development, effective antiviral treatments remain limited, particularly those targeting the papain-like protease (PLpro) of SARS-CoV-2. PLpro plays a key role in viral replication and immune [...] Read more.
Coronavirus disease 2019 (COVID-19) produced devastating health and economic impacts worldwide. While progress has been made in vaccine development, effective antiviral treatments remain limited, particularly those targeting the papain-like protease (PLpro) of SARS-CoV-2. PLpro plays a key role in viral replication and immune evasion, making it an attractive yet underexplored target for drug repurposing. In this study, we combined machine learning, molecular dynamics, and molecular docking to identify potential PLpro inhibitors in existing drugs. We performed long-timescale molecular dynamics simulations on PLpro–ligand complexes at two known binding sites, followed by structural clustering to capture representative structures. These were used for molecular docking, including a training set of 127 compounds and a library of 1107 FDA-approved drugs. A random forest model, trained on the docking scores of the representative conformations, yielded 76.4% accuracy via leave-one-out cross-validation. Applying the model to the drug library and filtering results based on prediction confidence and the applicability domain, we identified five drugs as promising candidates for repurposing for COVID-19 treatment. Our findings demonstrate the power of integrating computational modeling with machine learning to accelerate drug repurposing against emerging viral targets. Full article
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14 pages, 3013 KiB  
Article
Observation of a Relationship Between Orbital-Specific Molecular Similarity Index and Toxicity of Methylcarbamate Derivatives
by Sihan Long, Yuuki Onitsuka, Soichiro Nagao and Masahiko Takahashi
Molecules 2025, 30(14), 2947; https://doi.org/10.3390/molecules30142947 - 12 Jul 2025
Viewed by 189
Abstract
We report a computational investigation on the reachability of the molecular similarity index (MSI) approach for predicting the relative drug strength of methylcarbamate derivatives. Traditional MSI values have been obtained by calculating the overlap integral of total electron momentum densities between one molecule [...] Read more.
We report a computational investigation on the reachability of the molecular similarity index (MSI) approach for predicting the relative drug strength of methylcarbamate derivatives. Traditional MSI values have been obtained by calculating the overlap integral of total electron momentum densities between one molecule and another. Furthermore, we have proposed and tested orbital-specific MSI (OS-MSI) values, obtained by doing the same but with electron momentum densities of a selected molecular orbital (MO) such as the highest occupied MO (HOMO) and the lowest unoccupied MO (LUMO). In the calculations, a Boltzmann-weighted electron momentum density estimated by a theoretical probability distribution of rotamers was used, while the solvation effect was considered using the polarizable continuum model. It is shown that the traditional MSI values as well as the OS-MSI values for the HOMO do not have any correlation with experimental relative toxicity of the methylcarbamate derivatives. In contrast, it has been observed and found that the OS-MSI values for the LUMO exhibit a noticeable correlation with the experimental data. The reason behind this observation is discussed in relation to the drug reaction mechanism of the methylcarbamate derivatives. Full article
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