Machine Learning Approaches in Drug Molecule Design
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 September 2025 | Viewed by 16
Special Issue Editor
Interests: chemical informatics; bioinformatics; molecular informatics; drug design
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Machine learning (ML) has emerged as a powerful tool in drug molecule design, revolutionizing traditional approaches by enabling faster, more efficient, and cost-effective discovery processes. By leveraging large-scale biological and chemical data, ML algorithms can predict molecular properties, identify potential drug targets, and optimize lead compounds with improved accuracy. Techniques such as deep learning, support vector machines, and random forests have been widely applied in tasks like virtual screening, de novo drug design, and quantitative structure-activity relationship (QSAR) modeling. The integration of ML with computational chemistry and systems biology is not only accelerating the early stages of drug discovery but also enhancing precision in later stages, such as toxicity prediction and pharmacokinetics analysis. As data availability and algorithmic performance continue to improve, machine learning is expected to play an increasingly central role in rational drug design and personalized medicine.
This Special Issue aims to highlight cutting-edge molecular research that leverages machine learning approaches to advance drug discovery, with a particular emphasis on uncovering molecular properties, mechanisms, and interactions that underpin therapeutic innovation.
Dr. Bing Niu
Guest Editor
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Keywords
- machine learning
- drug design
- deep learning
- virtual screening
- QSAR
- molecular property prediction
- de novo drug design
- pharmacokinetics
- computational chemistry
- drug discovery
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