Machine Learning Strategies for Target and Non-target Chemical Compound Space
A special issue of Molecules (ISSN 1420-3049). This special issue belongs to the section "Computational and Theoretical Chemistry".
Deadline for manuscript submissions: closed (1 September 2023) | Viewed by 2844
Special Issue Editors
Interests: analytical chemistry; chemometrics
Special Issues, Collections and Topics in MDPI journals
Interests: computer chemistry; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advancements in machine learning have provided powerful tools for the exploration of the wide range of chemical compound space. The applicability of these machine learning methods creates the new avenues regarding the exploration of chemical compound space, starting from virtual sampling in the development and repurposing of new molecules to design materials with desired properties.
The analytical chemistry of various chemical species is a task of extreme importance. Performing target (the analytical determination of specific analyte(s)) or non-target (the determination of different analytes without specific aims for the determination) analysis is always a real analytical challenge. Various advanced instrumental methods are used to ensure the required analytical quality of the results obtained. Therefore, the correct performance of analytical procedures for the determination of organic and inorganic chemical compounds of different origins (contaminants from anthropogenic or natural sources, compounds for material science or green chemistry liquids and solvents, etc.) followed by the complete assessment of the methods applied for all aspects of quality assurance is one of the major goals of the present Special Issue.
Another important goal of this Special Issue is to invite researchers to contribute reliable estimations of monitoring data. This is usually achieved through the application of the methods united under the title of machine learning (ML) approaches characterized by their ability to classify and model large sets of monitoring data. The role of the multivariate statistical procedures known as chemometrics should not be underestimated as options for intelligent data interpretation. Research studies in these fields are also encouraged.
Dr. Vasil D. Simeonov
Dr. Miroslava A. Nedyalkova
Guest Editors
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Keywords
- machine learning
- chemical compound
- pollutants
- virtual screening
- docking
- multivariate statistics
- molecular dynamics
- QSPR
- target and non-target analysis
