Advances in Bioactive Molecules and Modern Structure–Activity Approaches

Dear Colleagues,

The discovery and optimization of bioactive molecules remain central challenges in pharmaceutical research, especially as scientists confront issues such as complex biological data, low hit-to-lead success rates, and the escalating time and cost of drug development. Advances in computational sciences—particularly artificial intelligence (AI), machine learning (ML), and modern structure–activity modeling—are reshaping this landscape and providing powerful tools to accelerate therapeutic innovation. Contemporary QSAR and 3D-QSAR methodologies, integrated with data-driven algorithms, now enable more accurate prediction of biological activity, improved characterization of chemical space, and more informed decision-making throughout the drug discovery pipeline. These approaches complement experimental strategies and enhance the identification, optimization, and evaluation of novel bioactive compounds. This Special Issue invites contributions that highlight recent progress in the design, analysis, and development of bioactive molecules, with a particular emphasis on structure–activity relationships and QSAR-based techniques. Original research articles exploring computational predictions, mechanistic insights, bioactivity modeling, or lead optimization are welcome. Review papers providing critical perspectives on methodological advancements, applications, or emerging trends in QSAR and bioactive molecule research are also encouraged.

Dr. Claudiu N. Lungu
Guest Editor

Manuscript Submission Information

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