Computational (In Silico) Approaches for Drug Target Discovery and Disposition
A special issue of Pharmaceuticals (ISSN 1424-8247).
Deadline for manuscript submissions: 20 August 2025 | Viewed by 802
Special Issue Editors
2. Simulations Plus, Lancaster, CA, USA
Interests: pharmacokinetics; pharmacodynamics
Special Issues, Collections and Topics in MDPI journals
Interests: DFT calculations; in silico methods; medicinal chemistry; natural products
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Computational approaches have revolutionized drug discovery by enabling the more efficient and accurate identification of potential therapeutic targets. In the past two decades, these in silico methods have become integral to rational drug design, facilitating a deeper understanding of complex biological systems and pharmacokinetic processes.
This Special Issue aims to showcase the latest advancements in and practical application of computational (in silico) tools throughout the process of drug development, from early target discovery to the creation of detailed pharmacokinetics and disposition models. We seek to foster cross-disciplinary collaborations among computational chemists, pharmacologists, biologists, and data scientists, thus driving innovation in predictive pharmacology and drug modeling. By featuring studies that integrate PBPK, PBBM, machine learning, and simulation, we hope to accelerate the design of safer and more effective therapeutics.
We welcome contributions that explore emerging computational techniques—such as AI-driven target identification, multi-scale modeling, and physiologically based modeling (PBPK, PBBM)—for drug disposition. We also welcome the submission of articles that demonstrate the integration of big data analytics or novel algorithms to address real-world challenges in drug development.
We welcome original research articles, methodological advances, and reviews that detail the development, validation, or application of computational tools to drug–target interactions, ADME/Tox properties, and translational research. We also encourage the submission of studies that highlight clinical relevance and collaborative efforts between experimental and computational disciplines.
Dr. Frederico Severino Martins
Dr. Leonardo Luiz Borges
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. 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 drug discovery
- in silico modeling
- drug target identification
- pharmacokinetics (pk)
- physiologically based pharmacokinetics (PBPK)
- physiologically based biokinetics (PBBK)
- physiologically based biopharmaceutics (PBBM)
- ADME/Tox
- machine learning in drug development
- systems pharmacology
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