Computational evaluation of some compounds as potential anti-breast cancer agents
Abstract Background The emergence of high resistance and toxicity of the existing anti-breast cancer drugs have demanded the need to design new drugs with improved activities against breast cancer. A computational technique incorporating quantitative structure–activity relationship and virtual templ...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2021-08-01
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| Series: | Future Journal of Pharmaceutical Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s43094-021-00315-2 |
| _version_ | 1818909430507372544 |
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| author | Momohjimoh Ovaku Idris Stephen Eyije Abechi Gideon Adamu Shallangwa |
| author_facet | Momohjimoh Ovaku Idris Stephen Eyije Abechi Gideon Adamu Shallangwa |
| author_sort | Momohjimoh Ovaku Idris |
| collection | DOAJ |
| description | Abstract Background The emergence of high resistance and toxicity of the existing anti-breast cancer drugs have demanded the need to design new drugs with improved activities against breast cancer. A computational technique incorporating quantitative structure–activity relationship and virtual template-based design was carried out to evaluate thirty-four compounds from derivatives of thiophene, pyrimidine, coumarin, pyrazole and pyridine with anti-breast cancer activities. The chemical structures of the compounds were drawn with chem draw v.12.0.2 and they were optimized using Spartan 14 software. The molecular descriptors were calculated with the aid of PaDel descriptor software. The dataset was curated and then divided into training and test set that was used to generate and validate the model. Results The first out of the four models generated was chosen as the paramount model with statistical validations of R 2 = 0.9847, $$R_{{{\text{adj}}}}^{2}$$ R adj 2 = 0.9814, $$Q_{{{\text{cv}}}}^{2}$$ Q cv 2 = 0.9763, min expt. error for non-significant LOF (95%) = 0.0679, an external validation $$R_{{{\text{test}}}}^{2}$$ R test 2 of 0.8240 and coefficient of Y-randomization ( $${\text{cR}}_{{\text{p}}}^{2}$$ cR p 2 ) = 0.8200, which confirm the robustness of the model. Conclusions The high predictive power of the generated model describes the models’ reliability and the designed compounds pointed out compound 2 with pGI50 = 4.2504 as the best designed compound to inhibit breast cancer, compared to its co-designed compounds and the template. The results of this research provide vital information to the pharmaceutical chemists and the pharmacologist in the course of developing new breast cancer drugs. |
| first_indexed | 2024-12-19T22:26:47Z |
| format | Article |
| id | doaj.art-e663a4f010864c00b1403e5200593844 |
| institution | Directory Open Access Journal |
| issn | 2314-7253 |
| language | English |
| last_indexed | 2024-12-19T22:26:47Z |
| publishDate | 2021-08-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Future Journal of Pharmaceutical Sciences |
| spelling | doaj.art-e663a4f010864c00b1403e52005938442022-12-21T20:03:30ZengSpringerOpenFuture Journal of Pharmaceutical Sciences2314-72532021-08-017111510.1186/s43094-021-00315-2Computational evaluation of some compounds as potential anti-breast cancer agentsMomohjimoh Ovaku Idris0Stephen Eyije Abechi1Gideon Adamu Shallangwa2Department of Chemistry, Ahmadu Bello UniversityDepartment of Chemistry, Ahmadu Bello UniversityDepartment of Chemistry, Ahmadu Bello UniversityAbstract Background The emergence of high resistance and toxicity of the existing anti-breast cancer drugs have demanded the need to design new drugs with improved activities against breast cancer. A computational technique incorporating quantitative structure–activity relationship and virtual template-based design was carried out to evaluate thirty-four compounds from derivatives of thiophene, pyrimidine, coumarin, pyrazole and pyridine with anti-breast cancer activities. The chemical structures of the compounds were drawn with chem draw v.12.0.2 and they were optimized using Spartan 14 software. The molecular descriptors were calculated with the aid of PaDel descriptor software. The dataset was curated and then divided into training and test set that was used to generate and validate the model. Results The first out of the four models generated was chosen as the paramount model with statistical validations of R 2 = 0.9847, $$R_{{{\text{adj}}}}^{2}$$ R adj 2 = 0.9814, $$Q_{{{\text{cv}}}}^{2}$$ Q cv 2 = 0.9763, min expt. error for non-significant LOF (95%) = 0.0679, an external validation $$R_{{{\text{test}}}}^{2}$$ R test 2 of 0.8240 and coefficient of Y-randomization ( $${\text{cR}}_{{\text{p}}}^{2}$$ cR p 2 ) = 0.8200, which confirm the robustness of the model. Conclusions The high predictive power of the generated model describes the models’ reliability and the designed compounds pointed out compound 2 with pGI50 = 4.2504 as the best designed compound to inhibit breast cancer, compared to its co-designed compounds and the template. The results of this research provide vital information to the pharmaceutical chemists and the pharmacologist in the course of developing new breast cancer drugs.https://doi.org/10.1186/s43094-021-00315-2Anti-breast cancerDatasetDataset divisionModel validationsTemplateDesign compounds |
| spellingShingle | Momohjimoh Ovaku Idris Stephen Eyije Abechi Gideon Adamu Shallangwa Computational evaluation of some compounds as potential anti-breast cancer agents Future Journal of Pharmaceutical Sciences Anti-breast cancer Dataset Dataset division Model validations Template Design compounds |
| title | Computational evaluation of some compounds as potential anti-breast cancer agents |
| title_full | Computational evaluation of some compounds as potential anti-breast cancer agents |
| title_fullStr | Computational evaluation of some compounds as potential anti-breast cancer agents |
| title_full_unstemmed | Computational evaluation of some compounds as potential anti-breast cancer agents |
| title_short | Computational evaluation of some compounds as potential anti-breast cancer agents |
| title_sort | computational evaluation of some compounds as potential anti breast cancer agents |
| topic | Anti-breast cancer Dataset Dataset division Model validations Template Design compounds |
| url | https://doi.org/10.1186/s43094-021-00315-2 |
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