Utilizing Molecular Descriptor Importance to Enhance Endpoint Predictions
Quantitative structure–activity relationship (QSAR) models are essential for predicting endpoints that are otherwise challenging to estimate using other in silico approaches. Developing interpretable models for endpoint prediction is valuable as interpretable models may provide valuable insights int...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Toxics |
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| Online Access: | https://www.mdpi.com/2305-6304/13/5/383 |
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| author | Benjamin Bajželj Marjana Novič Viktor Drgan |
| author_facet | Benjamin Bajželj Marjana Novič Viktor Drgan |
| author_sort | Benjamin Bajželj |
| collection | DOAJ |
| description | Quantitative structure–activity relationship (QSAR) models are essential for predicting endpoints that are otherwise challenging to estimate using other in silico approaches. Developing interpretable models for endpoint prediction is valuable as interpretable models may provide valuable insights into the relationship between molecular structure and observed biological or toxicological properties of compounds. In this study, we introduce a novel modification of counter-propagation artificial neural networks that aims to identify key molecular features responsible for classifying molecules into specific endpoint classes. The novel approach presented in this work dynamically adjusts molecular descriptor importance during model training, allowing different molecular descriptor importance values for structurally different molecules, which increases its adaptability to diverse sets of compounds. We applied the method to enzyme inhibition and hepatotoxicity classification datasets. Our findings show that the proposed approach improves the classification of molecules, reduces the number of neurons excited by molecules from different endpoint classes, and increases the number of acceptable models. The proposed approach may be useful in compound toxicity prediction and drug design studies. |
| format | Article |
| id | doaj-art-a04cd8fad6be498cabd0f92dda0eecb3 |
| institution | DOAJ |
| issn | 2305-6304 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Toxics |
| spelling | doaj-art-a04cd8fad6be498cabd0f92dda0eecb32025-08-20T03:12:04ZengMDPI AGToxics2305-63042025-05-0113538310.3390/toxics13050383Utilizing Molecular Descriptor Importance to Enhance Endpoint PredictionsBenjamin Bajželj0Marjana Novič1Viktor Drgan2Laboratory for Cheminformatics, Theory Department, National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, SloveniaLaboratory for Cheminformatics, Theory Department, National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, SloveniaLaboratory for Cheminformatics, Theory Department, National Institute of Chemistry, Hajdrihova 19, 1001 Ljubljana, SloveniaQuantitative structure–activity relationship (QSAR) models are essential for predicting endpoints that are otherwise challenging to estimate using other in silico approaches. Developing interpretable models for endpoint prediction is valuable as interpretable models may provide valuable insights into the relationship between molecular structure and observed biological or toxicological properties of compounds. In this study, we introduce a novel modification of counter-propagation artificial neural networks that aims to identify key molecular features responsible for classifying molecules into specific endpoint classes. The novel approach presented in this work dynamically adjusts molecular descriptor importance during model training, allowing different molecular descriptor importance values for structurally different molecules, which increases its adaptability to diverse sets of compounds. We applied the method to enzyme inhibition and hepatotoxicity classification datasets. Our findings show that the proposed approach improves the classification of molecules, reduces the number of neurons excited by molecules from different endpoint classes, and increases the number of acceptable models. The proposed approach may be useful in compound toxicity prediction and drug design studies.https://www.mdpi.com/2305-6304/13/5/383quantitative structure–activity relationshipmolecular descriptorsmolecular descriptor importanceenzyme inhibitionhepatotoxicity |
| spellingShingle | Benjamin Bajželj Marjana Novič Viktor Drgan Utilizing Molecular Descriptor Importance to Enhance Endpoint Predictions Toxics quantitative structure–activity relationship molecular descriptors molecular descriptor importance enzyme inhibition hepatotoxicity |
| title | Utilizing Molecular Descriptor Importance to Enhance Endpoint Predictions |
| title_full | Utilizing Molecular Descriptor Importance to Enhance Endpoint Predictions |
| title_fullStr | Utilizing Molecular Descriptor Importance to Enhance Endpoint Predictions |
| title_full_unstemmed | Utilizing Molecular Descriptor Importance to Enhance Endpoint Predictions |
| title_short | Utilizing Molecular Descriptor Importance to Enhance Endpoint Predictions |
| title_sort | utilizing molecular descriptor importance to enhance endpoint predictions |
| topic | quantitative structure–activity relationship molecular descriptors molecular descriptor importance enzyme inhibition hepatotoxicity |
| url | https://www.mdpi.com/2305-6304/13/5/383 |
| work_keys_str_mv | AT benjaminbajzelj utilizingmoleculardescriptorimportancetoenhanceendpointpredictions AT marjananovic utilizingmoleculardescriptorimportancetoenhanceendpointpredictions AT viktordrgan utilizingmoleculardescriptorimportancetoenhanceendpointpredictions |