Machine Learning in Molecular Sciences: From Molecular Descriptors to Neural Architectures
A topical collection in International Journal of Molecular Sciences (ISSN 1422-0067). This collection belongs to the section "Molecular Informatics".
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Interests: carbon nanomaterials; metal nanoparticles; metal oxides; electrochemical sensors; biosensors
Special Issues, Collections and Topics in MDPI journals
Topical Collection Information
Dear Colleagues,
Machine learning (ML) is no longer a future trend in molecular sciences; it is now a central force driving discovery, design, and decision-making across the field. This Special Issue aims to showcase the most recent advances in ML-powered research across all branches of molecular science, from data-driven molecular design to neural network models of spectroscopic and biological properties.
As the complexity and volume of experimental and computational data continue to grow, the role of ML becomes increasingly indispensable—not only in predictive modeling and simulation but also in signal interpretation, structure activity relationship modeling and the integration of large-scale omics and sensor datasets. Recent advances in chemoinformatics, electroanalytical chemistry, spectroscopy, and structural biology are now being enhanced by both traditional ML and modern deep learning approaches, including neural networks, transformers, and explainable AI.
This Special Issue seeks to bring together interdisciplinary contributions that demonstrate how ML can address real-world challenges in molecular research, whether through novel algorithms, innovative data processing workflows, or impactful applications in drug discovery, diagnostics, materials development, or sensing technologies.
Topics of interest include, but are not limited to, the following:
- ML-guided molecular modeling and simulation;
- Advanced chemoinformatics and deep QSAR/QSPR modeling;
- ML in spectroscopy: pattern recognition, signal processing, and calibration;
- ML applications in electrochemistry and electroanalytical data processing;
- Neural networks and transformer models in structural biology;
- ML based prediction of biomolecular interactions, docking, and folding;
- AI enhanced drug discovery and optimization;
- Chemometrics and interpretable machine learning approaches;
- Multiomics data integration using ML and deep learning;
- Novel ML architectures for molecular and analytical applications.
Prof. Dr. Robert Piech
Dr. Filip Ciepiela
Collection Editors
Manuscript Submission Information
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Keywords
- machine learning in molecular sciences
- deep learning and neural networks
- chemoinformatics and QSAR/QSPR
- spectroscopy and signal processing
- electrochemical data analysis
- biomolecular interaction prediction
- drug discovery and molecular design
- multiomics integration and big data
