Advances in Drug Discovery and Development: From Rational Design to Experimental Validation
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 March 2027 | Viewed by 3662
Special Issue Editor
Interests: AI-based drug design; molecular modeling; computational material; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Drug discovery and development remain central to pharmaceutical research. Traditionally, experimental and computational methods were pursued separately, but recent studies highlight the advantages of combining them to improve the design of new medicines. Experimental approaches such as biochemical assays and structural biology, together with computational methods, have long provided important insights into the discovery of candidate compounds. In recent years, advances in machine learning and deep learning have further expanded these capabilities by enabling the analysis of complex datasets and identifying patterns that accelerate the discovery of promising therapeutic candidates. The synergy between established laboratory methods and modern computational innovations is transforming how drug candidates are discovered, refined, and advanced toward preclinical and clinical evaluation.
Recognizing this rapid progress and the growing need to integrate diverse approaches, this Special Issue aims to provide a platform for sharing recent advances and emerging strategies in drug discovery and development. Accordingly, we invite original research articles, reviews, and short communications that span the full discovery-to-development spectrum. The topics of interest include target identification and validation, drug–target interactions, computational toxicology, medicinal chemistry, biotechnology-based therapeutics, and translational studies that link preclinical findings with clinical research. Particular emphasis will be given to contributions that combine computational predictions with experimental validation, such as, for example, docking- or simulation-guided design followed by in vitro or in vivo testing using MIC assays, MTT cytotoxicity evaluation, cell culture studies, or Western blot analysis. By linking predictive modeling, laboratory validation, and translational insights, this Special Issue seeks to promote efficient strategies for developing safer and more effective next-generation therapies.
Dr. Vikas Kumar
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. 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
- drug discovery
- drug development
- rational drug design
- molecular docking
- virtual screening
- pharmacophore modeling
- molecular dynamics simulations
- machine learning in drug discovery
- deep learning in drug discovery
- experimental validation
- structure–activity relationship
- bioactive compounds
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