Insights into the structure-activity relationship of pyrimidine-sulfonamide analogues for targeting BRAF V600E protein
B-rapidly accelerated fibrosarcoma (BRAF) V600E plays a crucial role in the progression of cutaneous melanoma. Core structures of BRAF V600E inhibitors are based on pyrimidine-sulfonamide scaffolds. Exploring the QSAR of these structures can improve our understanding of BRAF V600E inhibitor drug des...
Ausführliche Beschreibung
Autor*in: |
Srisongkram, Tarapong [verfasserIn] Tookkane, Dheerapat [verfasserIn] |
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Format: |
E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2024 |
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Schlagwörter: |
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Übergeordnetes Werk: |
Enthalten in: Biophysical chemistry - Amsterdam [u.a.] : Elsevier Science, 1973, 307 |
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Übergeordnetes Werk: |
volume:307 |
DOI / URN: |
10.1016/j.bpc.2024.107179 |
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Katalog-ID: |
ELV067211607 |
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520 | |a B-rapidly accelerated fibrosarcoma (BRAF) V600E plays a crucial role in the progression of cutaneous melanoma. Core structures of BRAF V600E inhibitors are based on pyrimidine-sulfonamide scaffolds. Exploring the QSAR of these structures can improve our understanding of BRAF V600E inhibitor drug design. This study utilized machine learning-based QSAR to elucidate chemical substructures of pyrimidine-sulfonamide analogues that correlated to the BRAF V600E inhibitory activity. The findings indicate that the support vector regression (SVR) combined with 15 fingerprints achieved the highest statistical performances in terms of goodness-of-fit, robustness, and predictability. Nine key fingerprints from pyrimidine-sulfonamide analogues were identified to exert the BRAF V600E inhibitory activity. These key fingerprints were validated using network-based activity cliff landscape and molecular docking. Together, the developed algorithm can serve as a screening tool for designing BRAF V600E inhibitors. To further utilize this model, we deployed our developed algorithm at https://qsarlabs.com/#braf. | ||
650 | 4 | |a Pyrimidine-sulfonamide | |
650 | 4 | |a BRAF V600E | |
650 | 4 | |a Machine learning | |
650 | 4 | |a Activity cliff | |
650 | 4 | |a Structure-activity relationship | |
700 | 1 | |a Tookkane, Dheerapat |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Biophysical chemistry |d Amsterdam [u.a.] : Elsevier Science, 1973 |g 307 |h Online-Ressource |w (DE-627)306321335 |w (DE-600)1496385-1 |w (DE-576)093980736 |x 1873-4200 |7 nnns |
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912 | |a GBV_ILN_2049 | ||
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912 | |a GBV_ILN_4393 | ||
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42.12 35.70 |
publishDate |
2024 |
allfields |
10.1016/j.bpc.2024.107179 doi (DE-627)ELV067211607 (ELSEVIER)S0301-4622(24)00008-5 DE-627 ger DE-627 rda eng 540 VZ 42.12 bkl 35.70 bkl Srisongkram, Tarapong verfasserin aut Insights into the structure-activity relationship of pyrimidine-sulfonamide analogues for targeting BRAF V600E protein 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier B-rapidly accelerated fibrosarcoma (BRAF) V600E plays a crucial role in the progression of cutaneous melanoma. Core structures of BRAF V600E inhibitors are based on pyrimidine-sulfonamide scaffolds. Exploring the QSAR of these structures can improve our understanding of BRAF V600E inhibitor drug design. This study utilized machine learning-based QSAR to elucidate chemical substructures of pyrimidine-sulfonamide analogues that correlated to the BRAF V600E inhibitory activity. The findings indicate that the support vector regression (SVR) combined with 15 fingerprints achieved the highest statistical performances in terms of goodness-of-fit, robustness, and predictability. Nine key fingerprints from pyrimidine-sulfonamide analogues were identified to exert the BRAF V600E inhibitory activity. These key fingerprints were validated using network-based activity cliff landscape and molecular docking. Together, the developed algorithm can serve as a screening tool for designing BRAF V600E inhibitors. To further utilize this model, we deployed our developed algorithm at https://qsarlabs.com/#braf. Pyrimidine-sulfonamide BRAF V600E Machine learning Activity cliff Structure-activity relationship Tookkane, Dheerapat verfasserin aut Enthalten in Biophysical chemistry Amsterdam [u.a.] : Elsevier Science, 1973 307 Online-Ressource (DE-627)306321335 (DE-600)1496385-1 (DE-576)093980736 1873-4200 nnns volume:307 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.12 Biophysik VZ 35.70 Biochemie: Allgemeines VZ AR 307 |
spelling |
10.1016/j.bpc.2024.107179 doi (DE-627)ELV067211607 (ELSEVIER)S0301-4622(24)00008-5 DE-627 ger DE-627 rda eng 540 VZ 42.12 bkl 35.70 bkl Srisongkram, Tarapong verfasserin aut Insights into the structure-activity relationship of pyrimidine-sulfonamide analogues for targeting BRAF V600E protein 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier B-rapidly accelerated fibrosarcoma (BRAF) V600E plays a crucial role in the progression of cutaneous melanoma. Core structures of BRAF V600E inhibitors are based on pyrimidine-sulfonamide scaffolds. Exploring the QSAR of these structures can improve our understanding of BRAF V600E inhibitor drug design. This study utilized machine learning-based QSAR to elucidate chemical substructures of pyrimidine-sulfonamide analogues that correlated to the BRAF V600E inhibitory activity. The findings indicate that the support vector regression (SVR) combined with 15 fingerprints achieved the highest statistical performances in terms of goodness-of-fit, robustness, and predictability. Nine key fingerprints from pyrimidine-sulfonamide analogues were identified to exert the BRAF V600E inhibitory activity. These key fingerprints were validated using network-based activity cliff landscape and molecular docking. Together, the developed algorithm can serve as a screening tool for designing BRAF V600E inhibitors. To further utilize this model, we deployed our developed algorithm at https://qsarlabs.com/#braf. Pyrimidine-sulfonamide BRAF V600E Machine learning Activity cliff Structure-activity relationship Tookkane, Dheerapat verfasserin aut Enthalten in Biophysical chemistry Amsterdam [u.a.] : Elsevier Science, 1973 307 Online-Ressource (DE-627)306321335 (DE-600)1496385-1 (DE-576)093980736 1873-4200 nnns volume:307 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.12 Biophysik VZ 35.70 Biochemie: Allgemeines VZ AR 307 |
allfields_unstemmed |
10.1016/j.bpc.2024.107179 doi (DE-627)ELV067211607 (ELSEVIER)S0301-4622(24)00008-5 DE-627 ger DE-627 rda eng 540 VZ 42.12 bkl 35.70 bkl Srisongkram, Tarapong verfasserin aut Insights into the structure-activity relationship of pyrimidine-sulfonamide analogues for targeting BRAF V600E protein 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier B-rapidly accelerated fibrosarcoma (BRAF) V600E plays a crucial role in the progression of cutaneous melanoma. Core structures of BRAF V600E inhibitors are based on pyrimidine-sulfonamide scaffolds. Exploring the QSAR of these structures can improve our understanding of BRAF V600E inhibitor drug design. This study utilized machine learning-based QSAR to elucidate chemical substructures of pyrimidine-sulfonamide analogues that correlated to the BRAF V600E inhibitory activity. The findings indicate that the support vector regression (SVR) combined with 15 fingerprints achieved the highest statistical performances in terms of goodness-of-fit, robustness, and predictability. Nine key fingerprints from pyrimidine-sulfonamide analogues were identified to exert the BRAF V600E inhibitory activity. These key fingerprints were validated using network-based activity cliff landscape and molecular docking. Together, the developed algorithm can serve as a screening tool for designing BRAF V600E inhibitors. To further utilize this model, we deployed our developed algorithm at https://qsarlabs.com/#braf. Pyrimidine-sulfonamide BRAF V600E Machine learning Activity cliff Structure-activity relationship Tookkane, Dheerapat verfasserin aut Enthalten in Biophysical chemistry Amsterdam [u.a.] : Elsevier Science, 1973 307 Online-Ressource (DE-627)306321335 (DE-600)1496385-1 (DE-576)093980736 1873-4200 nnns volume:307 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.12 Biophysik VZ 35.70 Biochemie: Allgemeines VZ AR 307 |
allfieldsGer |
10.1016/j.bpc.2024.107179 doi (DE-627)ELV067211607 (ELSEVIER)S0301-4622(24)00008-5 DE-627 ger DE-627 rda eng 540 VZ 42.12 bkl 35.70 bkl Srisongkram, Tarapong verfasserin aut Insights into the structure-activity relationship of pyrimidine-sulfonamide analogues for targeting BRAF V600E protein 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier B-rapidly accelerated fibrosarcoma (BRAF) V600E plays a crucial role in the progression of cutaneous melanoma. Core structures of BRAF V600E inhibitors are based on pyrimidine-sulfonamide scaffolds. Exploring the QSAR of these structures can improve our understanding of BRAF V600E inhibitor drug design. This study utilized machine learning-based QSAR to elucidate chemical substructures of pyrimidine-sulfonamide analogues that correlated to the BRAF V600E inhibitory activity. The findings indicate that the support vector regression (SVR) combined with 15 fingerprints achieved the highest statistical performances in terms of goodness-of-fit, robustness, and predictability. Nine key fingerprints from pyrimidine-sulfonamide analogues were identified to exert the BRAF V600E inhibitory activity. These key fingerprints were validated using network-based activity cliff landscape and molecular docking. Together, the developed algorithm can serve as a screening tool for designing BRAF V600E inhibitors. To further utilize this model, we deployed our developed algorithm at https://qsarlabs.com/#braf. Pyrimidine-sulfonamide BRAF V600E Machine learning Activity cliff Structure-activity relationship Tookkane, Dheerapat verfasserin aut Enthalten in Biophysical chemistry Amsterdam [u.a.] : Elsevier Science, 1973 307 Online-Ressource (DE-627)306321335 (DE-600)1496385-1 (DE-576)093980736 1873-4200 nnns volume:307 GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.12 Biophysik VZ 35.70 Biochemie: Allgemeines VZ AR 307 |
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Insights into the structure-activity relationship of pyrimidine-sulfonamide analogues for targeting BRAF V600E protein |
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Insights into the structure-activity relationship of pyrimidine-sulfonamide analogues for targeting BRAF V600E protein |
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Srisongkram, Tarapong |
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Biophysical chemistry |
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Srisongkram, Tarapong Tookkane, Dheerapat |
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insights into the structure-activity relationship of pyrimidine-sulfonamide analogues for targeting braf v600e protein |
title_auth |
Insights into the structure-activity relationship of pyrimidine-sulfonamide analogues for targeting BRAF V600E protein |
abstract |
B-rapidly accelerated fibrosarcoma (BRAF) V600E plays a crucial role in the progression of cutaneous melanoma. Core structures of BRAF V600E inhibitors are based on pyrimidine-sulfonamide scaffolds. Exploring the QSAR of these structures can improve our understanding of BRAF V600E inhibitor drug design. This study utilized machine learning-based QSAR to elucidate chemical substructures of pyrimidine-sulfonamide analogues that correlated to the BRAF V600E inhibitory activity. The findings indicate that the support vector regression (SVR) combined with 15 fingerprints achieved the highest statistical performances in terms of goodness-of-fit, robustness, and predictability. Nine key fingerprints from pyrimidine-sulfonamide analogues were identified to exert the BRAF V600E inhibitory activity. These key fingerprints were validated using network-based activity cliff landscape and molecular docking. Together, the developed algorithm can serve as a screening tool for designing BRAF V600E inhibitors. To further utilize this model, we deployed our developed algorithm at https://qsarlabs.com/#braf. |
abstractGer |
B-rapidly accelerated fibrosarcoma (BRAF) V600E plays a crucial role in the progression of cutaneous melanoma. Core structures of BRAF V600E inhibitors are based on pyrimidine-sulfonamide scaffolds. Exploring the QSAR of these structures can improve our understanding of BRAF V600E inhibitor drug design. This study utilized machine learning-based QSAR to elucidate chemical substructures of pyrimidine-sulfonamide analogues that correlated to the BRAF V600E inhibitory activity. The findings indicate that the support vector regression (SVR) combined with 15 fingerprints achieved the highest statistical performances in terms of goodness-of-fit, robustness, and predictability. Nine key fingerprints from pyrimidine-sulfonamide analogues were identified to exert the BRAF V600E inhibitory activity. These key fingerprints were validated using network-based activity cliff landscape and molecular docking. Together, the developed algorithm can serve as a screening tool for designing BRAF V600E inhibitors. To further utilize this model, we deployed our developed algorithm at https://qsarlabs.com/#braf. |
abstract_unstemmed |
B-rapidly accelerated fibrosarcoma (BRAF) V600E plays a crucial role in the progression of cutaneous melanoma. Core structures of BRAF V600E inhibitors are based on pyrimidine-sulfonamide scaffolds. Exploring the QSAR of these structures can improve our understanding of BRAF V600E inhibitor drug design. This study utilized machine learning-based QSAR to elucidate chemical substructures of pyrimidine-sulfonamide analogues that correlated to the BRAF V600E inhibitory activity. The findings indicate that the support vector regression (SVR) combined with 15 fingerprints achieved the highest statistical performances in terms of goodness-of-fit, robustness, and predictability. Nine key fingerprints from pyrimidine-sulfonamide analogues were identified to exert the BRAF V600E inhibitory activity. These key fingerprints were validated using network-based activity cliff landscape and molecular docking. Together, the developed algorithm can serve as a screening tool for designing BRAF V600E inhibitors. To further utilize this model, we deployed our developed algorithm at https://qsarlabs.com/#braf. |
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title_short |
Insights into the structure-activity relationship of pyrimidine-sulfonamide analogues for targeting BRAF V600E protein |
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up_date |
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