6D-QSAR for predicting biological activity of human aldose reductase inhibitors using quasar receptor surface modeling
Abstract The application of QSAR analysis dates back a half-century ago and is currently continuously employed in any rational drug design. The multi-dimensional QSAR modeling can be a promising tool for researchers to develop reliable predictive QSAR models for designing novel compounds. In the pre...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2023-06-01
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| Series: | BMC Chemistry |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13065-023-00970-x |
| _version_ | 1827917213331357696 |
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| author | Babak Sokouti Maryam Hamzeh-Mivehroud |
| author_facet | Babak Sokouti Maryam Hamzeh-Mivehroud |
| author_sort | Babak Sokouti |
| collection | DOAJ |
| description | Abstract The application of QSAR analysis dates back a half-century ago and is currently continuously employed in any rational drug design. The multi-dimensional QSAR modeling can be a promising tool for researchers to develop reliable predictive QSAR models for designing novel compounds. In the present work, we studied inhibitors of human aldose reductase (AR) to generate multi-dimensional QSAR models using 3D- and 6D-QSAR methods. For this purpose, Pentacle and Quasar’s programs were used to produce the QSAR models using corresponding dissociation constant (Kd) values. By inspecting the performance metrics of the generated models, we achieved similar results with comparable internal validation statistics. However, considering the externally validated values, 6D-QSAR models provide significantly better prediction of endpoint values. The obtained results suggest that the higher the dimension of the QSAR model, the higher the performance of the generated model. However, more studies are required to verify these outcomes. |
| first_indexed | 2024-03-13T03:24:40Z |
| format | Article |
| id | doaj.art-d766d3a6e3004382ba5614949f8f55eb |
| institution | Directory Open Access Journal |
| issn | 2661-801X |
| language | English |
| last_indexed | 2024-03-13T03:24:40Z |
| publishDate | 2023-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Chemistry |
| spelling | doaj.art-d766d3a6e3004382ba5614949f8f55eb2023-06-25T11:07:55ZengBMCBMC Chemistry2661-801X2023-06-011711910.1186/s13065-023-00970-x6D-QSAR for predicting biological activity of human aldose reductase inhibitors using quasar receptor surface modelingBabak Sokouti0Maryam Hamzeh-Mivehroud1Biotechnology Research Center, Tabriz University of Medical SciencesBiotechnology Research Center, Tabriz University of Medical SciencesAbstract The application of QSAR analysis dates back a half-century ago and is currently continuously employed in any rational drug design. The multi-dimensional QSAR modeling can be a promising tool for researchers to develop reliable predictive QSAR models for designing novel compounds. In the present work, we studied inhibitors of human aldose reductase (AR) to generate multi-dimensional QSAR models using 3D- and 6D-QSAR methods. For this purpose, Pentacle and Quasar’s programs were used to produce the QSAR models using corresponding dissociation constant (Kd) values. By inspecting the performance metrics of the generated models, we achieved similar results with comparable internal validation statistics. However, considering the externally validated values, 6D-QSAR models provide significantly better prediction of endpoint values. The obtained results suggest that the higher the dimension of the QSAR model, the higher the performance of the generated model. However, more studies are required to verify these outcomes.https://doi.org/10.1186/s13065-023-00970-xBiological activityMolecular descriptorsMultidimensional QSARHuman aldose reductase inhibitors6D-QSAR |
| spellingShingle | Babak Sokouti Maryam Hamzeh-Mivehroud 6D-QSAR for predicting biological activity of human aldose reductase inhibitors using quasar receptor surface modeling BMC Chemistry Biological activity Molecular descriptors Multidimensional QSAR Human aldose reductase inhibitors 6D-QSAR |
| title | 6D-QSAR for predicting biological activity of human aldose reductase inhibitors using quasar receptor surface modeling |
| title_full | 6D-QSAR for predicting biological activity of human aldose reductase inhibitors using quasar receptor surface modeling |
| title_fullStr | 6D-QSAR for predicting biological activity of human aldose reductase inhibitors using quasar receptor surface modeling |
| title_full_unstemmed | 6D-QSAR for predicting biological activity of human aldose reductase inhibitors using quasar receptor surface modeling |
| title_short | 6D-QSAR for predicting biological activity of human aldose reductase inhibitors using quasar receptor surface modeling |
| title_sort | 6d qsar for predicting biological activity of human aldose reductase inhibitors using quasar receptor surface modeling |
| topic | Biological activity Molecular descriptors Multidimensional QSAR Human aldose reductase inhibitors 6D-QSAR |
| url | https://doi.org/10.1186/s13065-023-00970-x |
| work_keys_str_mv | AT babaksokouti 6dqsarforpredictingbiologicalactivityofhumanaldosereductaseinhibitorsusingquasarreceptorsurfacemodeling AT maryamhamzehmivehroud 6dqsarforpredictingbiologicalactivityofhumanaldosereductaseinhibitorsusingquasarreceptorsurfacemodeling |