Molecular Structural Characteristics Important in Drug-HSA Binding
Апстракт
A non-linear quantitative structure activity relationship (QSAR) model based on 350 drug molecules was developed as a predictive tool for drug protein binding, by correlating experimentally measured protein binding values with ten calculated molecular descriptors using a radial basis function (RBF) neural network. The developed model has a statistically significant overall correlation value (r > 0.73), a high efficiency ratio (0.986), and a good predictive squared correlation coefficient (q(2)) of 0.532, which is regarded as producing a robust and high quality QSAR model. The developed model may be used for the screening of drug candidate molecules that have high protein binding data, filtering out compounds that are unlikely to be protein bound, and may assist in the dose adjustment for drugs that are highly protein bound. The advantage of using such a model is that the percentage of a potential drug candidate that is protein bound (PB (%)) can be simply predicted from its molecular s...tructure.
Кључне речи:
ANN / drug-protein binding / in silico modelling / QSAR / screening / theoretical molecular descriptorsИзвор:
Combinatorial Chemistry & High Throughput Screening, 2014, 17, 10, 879-890Издавач:
- Bentham Science Publ Ltd, Sharjah
DOI: 10.2174/1386207317666141114222955
ISSN: 1386-2073
PubMed: 25410275
WoS: 000347779300008
Scopus: 2-s2.0-84926342597
Институција/група
PharmacyTY - JOUR AU - Agatonović-Kuštrin, Snežana AU - Morton, David W. AU - Truong, Lisa AU - Ražić, Slavica PY - 2014 UR - https://farfar.pharmacy.bg.ac.rs/handle/123456789/2124 AB - A non-linear quantitative structure activity relationship (QSAR) model based on 350 drug molecules was developed as a predictive tool for drug protein binding, by correlating experimentally measured protein binding values with ten calculated molecular descriptors using a radial basis function (RBF) neural network. The developed model has a statistically significant overall correlation value (r > 0.73), a high efficiency ratio (0.986), and a good predictive squared correlation coefficient (q(2)) of 0.532, which is regarded as producing a robust and high quality QSAR model. The developed model may be used for the screening of drug candidate molecules that have high protein binding data, filtering out compounds that are unlikely to be protein bound, and may assist in the dose adjustment for drugs that are highly protein bound. The advantage of using such a model is that the percentage of a potential drug candidate that is protein bound (PB (%)) can be simply predicted from its molecular structure. PB - Bentham Science Publ Ltd, Sharjah T2 - Combinatorial Chemistry & High Throughput Screening T1 - Molecular Structural Characteristics Important in Drug-HSA Binding VL - 17 IS - 10 SP - 879 EP - 890 DO - 10.2174/1386207317666141114222955 UR - https://hdl.handle.net/21.15107/rcub_farfar_2124 ER -
@article{ author = "Agatonović-Kuštrin, Snežana and Morton, David W. and Truong, Lisa and Ražić, Slavica", year = "2014", abstract = "A non-linear quantitative structure activity relationship (QSAR) model based on 350 drug molecules was developed as a predictive tool for drug protein binding, by correlating experimentally measured protein binding values with ten calculated molecular descriptors using a radial basis function (RBF) neural network. The developed model has a statistically significant overall correlation value (r > 0.73), a high efficiency ratio (0.986), and a good predictive squared correlation coefficient (q(2)) of 0.532, which is regarded as producing a robust and high quality QSAR model. The developed model may be used for the screening of drug candidate molecules that have high protein binding data, filtering out compounds that are unlikely to be protein bound, and may assist in the dose adjustment for drugs that are highly protein bound. The advantage of using such a model is that the percentage of a potential drug candidate that is protein bound (PB (%)) can be simply predicted from its molecular structure.", publisher = "Bentham Science Publ Ltd, Sharjah", journal = "Combinatorial Chemistry & High Throughput Screening", title = "Molecular Structural Characteristics Important in Drug-HSA Binding", volume = "17", number = "10", pages = "879-890", doi = "10.2174/1386207317666141114222955", url = "https://hdl.handle.net/21.15107/rcub_farfar_2124" }
Agatonović-Kuštrin, S., Morton, D. W., Truong, L.,& Ražić, S.. (2014). Molecular Structural Characteristics Important in Drug-HSA Binding. in Combinatorial Chemistry & High Throughput Screening Bentham Science Publ Ltd, Sharjah., 17(10), 879-890. https://doi.org/10.2174/1386207317666141114222955 https://hdl.handle.net/21.15107/rcub_farfar_2124
Agatonović-Kuštrin S, Morton DW, Truong L, Ražić S. Molecular Structural Characteristics Important in Drug-HSA Binding. in Combinatorial Chemistry & High Throughput Screening. 2014;17(10):879-890. doi:10.2174/1386207317666141114222955 https://hdl.handle.net/21.15107/rcub_farfar_2124 .
Agatonović-Kuštrin, Snežana, Morton, David W., Truong, Lisa, Ražić, Slavica, "Molecular Structural Characteristics Important in Drug-HSA Binding" in Combinatorial Chemistry & High Throughput Screening, 17, no. 10 (2014):879-890, https://doi.org/10.2174/1386207317666141114222955 ., https://hdl.handle.net/21.15107/rcub_farfar_2124 .