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Biomedicines
Special Issues
Advances in Drug Discovery and Development Using Mass Spectrometry
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Advances in Drug Discovery and Development Using Mass Spectrometry

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Drug Discovery, Development and Delivery".

Deadline for manuscript submissions: 31 May 2026 | Viewed by 975

Special Issue Editors

Dr. Emily G. Werth

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Guest Editor
Drug Metabolism and Pharmacokinetics, Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT 06877, USA
Interests: DMPK; proteomics; mass spectrometry; bioanalytics; drug development
Dr. Amirata Saei Dibavar

E-Mail Website
Guest Editor
Centre for Translational Microbiome Research, Department of Microbiology, Tumor and Cell Biology, Karolinska Institutet, 171 65 Stockholm, Sweden
Interests: enzymes; proteomics; cancer metabolism; mass spectrometry; drug discovery
Special Issues, Collections and Topics in MDPI journals
Dr. Lin-Zhi Chen

E-Mail Website
Guest Editor
Boehringer Ingelheim Pharmaceuticals, Inc., Ridgefield, CT 06877, USA
Interests: DMPK; proteomics; mass spectrometry; bioanalytics; drug development

Special Issue Information

Dear Colleagues,

Proteomics-based methods are widely used across the pharmaceutical industry, aiming to identify, characterize, and/or quantify new drug therapeutics. In the early stages of research, exploratory proteomic strategies enable scientists to focus in on significant signaling pathways and identify protein targets crucial to disease states. Later in the process, once potential candidates are identified, efforts to characterize them through assessing critical quality attributes for developability highlight the risks and benefits of potential programs. Once a candidate is considered desirable for the development investment, targeted proteomic approaches can provide the drug’s pharmacokinetics or quantify relevant biomarkers in a highly regulated environment. The proteomics field has had a strong impact, from idea to filing, on the pharmaceutical industry and continues to grow as new innovative techniques are revealed.

Acknowledging the diverse utility of proteomics to pharmaceutical sciences, I invite authors to submit original research and review articles on the topic: Advances in Drug Discovery and Development Using Mass Spectrometry.

Dr. Emily G. Werth
Dr. Amirata Saei Dibavar
Dr. Lin-Zhi Chen
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 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. Biomedicines 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 2600 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

  • LC/MS
  • proteomics
  • protein therapeutics
  • PTMs
  • immunocapture
  • CQAs

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Published Papers (1 paper)

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Research

15 pages, 3463 KB  
Article
LLM-Enhanced Multimodal Framework for Drug–Drug Interaction Prediction
by Song Im and Younhee Ko
Biomedicines 2025, 13(10), 2355; https://doi.org/10.3390/biomedicines13102355 - 26 Sep 2025
Viewed by 581
Abstract
Background: Drug–drug interactions (DDIs) involve pharmacokinetic or pharmacodynamic changes that occur when multiple drugs are co-administered, potentially leading to reduced efficacy or adverse effects. As polypharmacy becomes more prevalent, especially among patients with chronic diseases, scalable and accurate DDI prediction has become increasingly [...] Read more.
Background: Drug–drug interactions (DDIs) involve pharmacokinetic or pharmacodynamic changes that occur when multiple drugs are co-administered, potentially leading to reduced efficacy or adverse effects. As polypharmacy becomes more prevalent, especially among patients with chronic diseases, scalable and accurate DDI prediction has become increasingly important. Although numerous computational approaches have been proposed to predict DDIs using various modalities such as chemical structure and biological networks, the intrinsic heterogeneity of these data complicates unified modeling; Methods: We address this challenge with a multimodal deep learning framework that integrates three complementary, heterogeneous modalities: (i) chemical structure, (ii) BioBERT-derived semantic embeddings (a domain-specific large language model, LLM), and (iii) pharmacological mechanisms through the CTET proteins. To incorporate indirect biological pathways within the PPI network, we apply a random walk with restart (RWR) algorithm. Results: Across features combinations, fusing structural feature with BioBERT embedding achieved the highest classification accuracy (0.9655), highlighting the value of readily available data and the capacity of domain-specific language models to encode pharmacological semantics from unstructured texts. Conclusions: BioBERT embeddings were particularly informative, capturing subtle pharmacological relationships between drugs and improving prediction of potential DDIs. Beyond predictive performance, the framework is readily applicable to real-world clinical workflows, providing rapid DDI references to support the polypharmacy decision-making. Full article
(This article belongs to the Special Issue Advances in Drug Discovery and Development Using Mass Spectrometry)
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