In Silico Drug Design and Virtual Screening: The Latest Advances
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".
Deadline for manuscript submissions: 30 October 2026 | Viewed by 57
Special Issue Editors
Interests: computational chemistry; chemoinformatics; drug discovery; molecular simulation; medicinal chemistry; drug repurposing; molecular docking; natural product-based drug discovery; QSAR modeling; artificial intelligence in drug discovery
Special Issues, Collections and Topics in MDPI journals
Interests: computational chemistry; chemoinformatics; drug discovery; QSAR modeling; molecular simulation; drug repurposing; molecular docking; natural products-based drug discovery; and artificial intelligence in drug discovery
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Advances in computational sciences are transforming drug discovery by accelerating the identification, optimization, and evaluation of promising therapeutic candidates. In silico drug design and virtual screening have become essential components of modern pharmaceutical research, supporting the efficient exploration of chemical space, the prediction of molecular interactions, the prioritization of biological targets, and the rational refinement of lead compounds. Recent progress in artificial intelligence, machine learning, and computer-aided drug design has further enhanced the efficiency, predictive accuracy, and translational value of these approaches.
We invite you to contribute to this Special Issue entitled “In Silico Drug Design and Virtual Screening: The Latest Advances”. The growing importance of this field lies in its ability to deliver advanced computational frameworks for elucidating biomolecular interactions, identifying promising lead compounds, and accelerating drug discovery and the development of innovative therapeutic solutions.
This Special Issue will highlight the latest methodological advances and innovative applications regarding in silico drug design and virtual screening, emphasizing research contributing to more efficient and reliable therapeutic discovery.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- In silico drug design;
- Virtual screening;
- Computer-aided drug design;
- Artificial intelligence and machine learning in drug discovery;
- Biomolecular recognition and target-binding studies;
- ADMET and toxicity prediction;
- Drug repurposing;
- Natural products, peptides, and biologics;
- Experimental validation of in silico predictions.
I look forward to receiving your contributions.
Dr. Alina Bora
Dr. Luminita Crisan
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 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. International Journal of Molecular Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
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Keywords
- in silico drug design
- drug discovery
- virtual screening
- CADD
- artificial intelligence
- machine learning
- ADMET prediction
- cheminformatics
- drug repurposing
- natural products
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