Special Issue "QSAR and Chemoinformatics Tools for Modeling"
Deadline for manuscript submissions: 30 June 2019
Prof. Dr. Roberto Todeschini
Milano Chemometrics and QSAR Research Group, Department of Environmental Sciences, University of Milano-Bicocca, Milano 20126, Italy
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Phone: +39 02 64482820
Fax: +39 02 64482839
Interests: chemometric, QSAR/QSPR, multi-criteria decision making, molecular descriptors, software development
In the past decade, quantitative structure–activity relationships (QSARs) have become a well-established field of scientific research, a field where many different mathematical tools are applied to detect predictive relationships between molecular structure and pharmacological activities, toxicological/ecotoxicological properties, and adverse effects of molecules on human health.
In the proposed Special Issue, the main idea is not only to present QSAR results on new datasets/modelling campaigns, but also to compare different chemometric and chemoinformatic tools on benchmark data sets, especially including (together with the classical regression and classification methods) read-across approaches, ranking models, machine learning, and deep learning methods.
Authors are also invited to pay attention to the concept of the applicability domain of the models, their prediction ability, and models obtained by data fusion and consensus approaches.
Molecular applications aimed to model endocrine disruptors effects, carcinogenicity, and mutagenicity as well as studies on omics data will be particularly appreciated.
Prof. Dr. Roberto Todeschini
Manuscript Submission Information
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- machine learning
- applicability domain
- regression models
- classification models
- ranking models
- consensus models
- molecular descriptors
- omics data
- endocrine disruptors