MoDeSuS : A Machine Learning Tool for Selection of Molecular Descriptors in QSAR Studies Applied to Molecular Informatics
The selection of the most relevant molecular descriptors to describe a target variable in the context of QSAR (Quantitative Structure-Activity Relationship) modelling is a challenging combinatorial optimization problem. In this paper, a novel software tool for addressing this task in the context of regression and classification modelling is presented. The methodology that implements the tool is organized into two phases. The first phase uses a multiobjective evolutionary technique to perform the selection of subsets of descriptors. The second phase performs an external validation of the chosen descriptors subsets in order to improve reliability. The tool functionalities have been illustrated through a case study for the estimation of the ready biodegradation property as an example of classification QSAR modelling. The results obtained show the usefulness and potential of this novel software tool that aims to reduce the time and costs of development in the drug discovery process.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2019 |
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| Enthalten in: |
BioMed research international - 2019(2019) vom: 11., Seite 2905203 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Martínez, María Jimena [VerfasserIn] |
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| Links: |
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| Themen: |
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| Anmerkungen: |
Date Completed 16.07.2019 Date Revised 06.10.2023 published: Electronic-eCollection Citation Status MEDLINE |
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| doi: |
10.1155/2019/2905203 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM295292598 |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a The selection of the most relevant molecular descriptors to describe a target variable in the context of QSAR (Quantitative Structure-Activity Relationship) modelling is a challenging combinatorial optimization problem. In this paper, a novel software tool for addressing this task in the context of regression and classification modelling is presented. The methodology that implements the tool is organized into two phases. The first phase uses a multiobjective evolutionary technique to perform the selection of subsets of descriptors. The second phase performs an external validation of the chosen descriptors subsets in order to improve reliability. The tool functionalities have been illustrated through a case study for the estimation of the ready biodegradation property as an example of classification QSAR modelling. The results obtained show the usefulness and potential of this novel software tool that aims to reduce the time and costs of development in the drug discovery process | ||
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| 700 | 1 | |a Ponzoni, Ignacio |e verfasserin |4 aut | |
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