In Silico Methods of Assessing the Therapeutic Potential of Natural Substances
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: 20 June 2025 | Viewed by 1297
Special Issue Editors
2. Department of Medical Informatics and Statistics, Medical University of Lodz, 90-645 Lodz, Poland
Interests: in silico screening; natural products; molecular docking; computational drug discovery; QSAR modeling; molecular dynamics simulations; natural compounds; drug-target interactions; virtual screening; structure-activity relationships; computational chemistry; cheminformatics; ADMET prediction; bioinformatics; phytochemicals; machine learning in drug discovery; computer-aided drug design; pharmacophore modeling; natural therapeutics; computational pharmacology
Special Issues, Collections and Topics in MDPI journals
2. Department of Allergology and Respiratory Rehabilitation, Medical University of Lodz, 90-725 Lodz, Poland
Interests: in silico screening; natural products; molecular docking; computational drug discovery; QSAR modeling; molecular dynamics simulations; natural compounds; drug-target interactions; virtual screening; structure-activity relationships; computational chemistry; cheminformatics; ADMET prediction; bioinformatics; phytochemicals; machine learning in drug discovery; computer-aided drug design; pharmacophore modeling; natural therapeutics; computational pharmacology
Special Issue Information
Dear Colleagues,
The exploration of naturally occurring substances for therapeutic applications has entered a new era with the advancement of computational methods. This Special Issue focuses on cutting-edge in silico approaches for evaluating the therapeutic potential of natural compounds, bringing together research that bridges traditional natural product discovery with modern computational techniques. We welcome studies involving molecular docking, machine learning applications, QSAR modeling, molecular dynamics simulations, and other computational methods that advance our understanding of natural substances' biological activities and drug-like properties. The issue particularly emphasizes research combining multiple computational approaches to predict, analyze, and optimize the therapeutic potential of compounds derived from natural sources, including plants. Original research articles, comprehensive reviews, and methodological papers addressing challenges and innovations in computational assessment of natural products are encouraged. Topics of interest include, but are not limited to, virtual screening of natural compound libraries, prediction of ADMET properties, target identification, and structure-activity relationship analyses. This Special Issue aims to showcase how in silico methods can accelerate the discovery and development of natural substance-based therapeutics while reducing the time and resources required for traditional experimental approaches.
Prof. Dr. Radosław Zajdel
Dr. Anna Merecz-Sadowska
Guest Editors
Manuscript Submission Information
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Keywords
- in silico screening
- natural products
- molecular docking
- computational drug discovery
- QSAR modeling
- molecular dynamics simulations
- natural compounds
- drug-target interactions
- virtual screening
- structure-activity relationships
- computational chemistry
- cheminformatics
- ADMET prediction
- bioinformatics
- phytochemicals
- machine learning in drug discovery
- computer-aided drug design
- pharmacophore modeling
- natural therapeutics
- computational pharmacology
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