Bioactive Molecules: Structure-Activity Relationship
A special issue of Current Issues in Molecular Biology (ISSN 1467-3045). This special issue belongs to the section "Biochemistry, Molecular and Cellular Biology".
Deadline for manuscript submissions: closed (10 April 2025) | Viewed by 6962
Special Issue Editor
Interests: medicinal chemistry; drug discovery; molecular modeling; molecular descriptors
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Due to low efficacy, off-target delivery, time consumption, and high costs, Drug design and development pose significant challenges for pharmaceutical firms and scientists. Additionally, the complexity and scale of data from genomics, proteomics, microarrays, and clinical trials further complicate drug discovery. However, artificial intelligence (AI) and machine learning (ML) technologies, including artificial neural networks and deep learning algorithms, have revolutionized this field, offering promising solutions. Artificial intelligence principles have been utilized in various aspects of drug development, including classifying active and inactive compounds, monitoring drug release, conducting pre-clinical and clinical development, screening drugs in primary and secondary stages, developing biomarkers, optimizing pharmaceutical manufacturing, identifying bioactivity and physiochemical properties, predicting toxicity, and determining the mode of action. In this view, the search for bioactive molecules is further expanded. Quantitative Structure-Activity Relationship (QSAR) serves as a computational tool to uncover connections between the structural features of chemical compounds and their biological activities, pivotal in drug discovery. QSAR methodologies, including the latest advancements in 3D-QSAR, offer significant benefits during the lead optimization stage of drug discovery. They complement bioinformatics and leverage the increasing accessibility of data. Overall, the structure-activity relationship remains the primary tool for screening, identifying, and selecting bioactive molecules.
This special issue addresses all bioactive molecules, especially QSAR-based methodologies (but not limited to QSAR) used to identify or further develop these compounds.
Research articles, including bioactive molecules and related QSAR studies, are welcome. Also, reviews regarding bioactive molecules and QSAR-based methodologies are further appreciated.
Dr. Claudiu N. Lungu
Guest Editor
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Keywords
- bioactive molecules
- quantitative structure activity relationship (QSAR)
- QSAR based methodologies
- QSAR multi-target
- drug design and discovery
- drug repurposing
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