The Experts below are selected from a list of 258 Experts worldwide ranked by ideXlab platform
Gerd B. Rocha - One of the best experts on this subject based on the ideXlab platform.
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Determining the Relative Binding Affinity of Ricin Toxin A Inhibitors by Using Molecular Docking and Nonequilibrium Work
Journal of chemical information and modeling, 2018Co-Authors: Elton José Ferreira Chaves, Itácio Q. M. Padilha, Demetrius A. M. Araújo, Gerd B. RochaAbstract:Ricin is a ribosome-inactivating protein (RIP type 2) consisting of two subunits, ricin toxin A (RTA) and ricin toxin B (RTB). Because of its cytotoxicity, ricin has worried world authorities for its potential use as a chemical weapon; therefore, its inhibition is of great biotechnological interest. RTA is the target for inhibitor synthesis, and pterin derivatives are promising candidates to inhibit it. In this study, we used a combination of the molecular docking approach and fast steered molecular dynamics (SMD) to assess the correlation between nonequilibrium work, ⟨W⟩, and the IC50 for six RTA inhibitors. The results showed that molecular docking is a powerful tool to predict good bioactive poses of RTA inhibitors, and ⟨W⟩ presented a strong correlation with IC50 (R2 = 0.961). Such a profile ranked the RTA inhibitors better than the molecular docking approach. Therefore, the combination of docking and fast SMD simulation was shown to be a promising tool to distinguish RTA-active inhibitors from inacti...
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Determining the Relative Binding Affinity of Ricin Toxin A Inhibitors by Using Molecular Docking and Nonequilibrium Work
2018Co-Authors: Elton J. F. Chaves, Itácio Q. M. Padilha, Demétrius A. M. Araújo, Gerd B. RochaAbstract:Ricin is a ribosome-inactivating protein (RIP type 2) consisting of two subunits, ricin toxin A (RTA) and ricin toxin B (RTB). Because of its cytotoxicity, ricin has worried world authorities for its potential use as a chemical weapon; therefore, its inhibition is of great biotechnological interest. RTA is the target for inhibitor synthesis, and pterin derivatives are promising candidates to inhibit it. In this study, we used a combination of the molecular docking approach and fast steered molecular dynamics (SMD) to assess the correlation between nonequilibrium work, ⟨W⟩, and the IC50 for six RTA inhibitors. The results showed that molecular docking is a powerful tool to predict good bioactive poses of RTA inhibitors, and ⟨W⟩ presented a strong correlation with IC50 (R2 = 0.961). Such a profile ranked the RTA inhibitors better than the molecular docking approach. Therefore, the combination of docking and fast SMD simulation was shown to be a promising tool to distinguish RTA-active inhibitors from inactive ones and could be used as postdocking filtering approach
Eric S. Manas - One of the best experts on this subject based on the ideXlab platform.
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GRAM: A True Null Model for Relative Binding Affinity Predictions.
Journal of chemical information and modeling, 2020Co-Authors: Guanglei Cui, Alan P. Graves, Eric S. ManasAbstract:Relative Binding Affinity prediction is a critical component in computer aided drug design. A significant amount of effort has been dedicated to developing rapid and reliable in silico methods. However, robust assessment of their performance is still a complicated issue, as it requires a performance measure applicable in the prospective setting and more importantly a true null model that defines the expected performance of being random in an objective manner. Although many performance metrics, such as the Pearson correlation coefficient (r), mean unsigned error (MUE), and root-mean-square error (RMSE), are frequently used in the literature, a true and nontrivial null model has yet been identified. To address this problem, here we introduce an interval estimate as an additional measure, namely, the prediction interval (PI), which can be estimated from the error distribution of the predictions. The benefits of using the interval estimate are (1) it provides the uncertainty range in the predicted activities, which is important in prospective applications, and (2) a true null model with well-defined PI can be established. We provide one such example termed the Gaussian Random Affinity Model (GRAM), which is based on the empirical observation that the Affinity change in a typical lead optimization effort has the tendency to distribute normally N (0, σ). Having an analytically defined PI that only depends on the variation in the activities, GRAM should, in principle, allow us to compare the performance of Relative Binding Affinity prediction methods in a standard way, ultimately critical to measuring the progress made in algorithm development.
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GRAM: A True Null Model for Relative Binding Affinity Predictions
2019Co-Authors: Guanglei Cui, Alan P. Graves, Eric S. ManasAbstract:Relative Binding Affinity prediction is a critical component in computer aided drug design. Significant amount of effort has been dedicated to developing rapid and reliable in silico methods. However, robust assessment of their performance is still a complicated issue, as it requires a performance measure applicable in the prospective setting and more importantly a true null model that defines the expected performance of random in an objective manner. Although many performance metrics, such as correlation coefficient (r2), mean unsigned error (MUE), and room mean square error (RMSE), are frequently used in the literature, a true and non-trivial null model has yet been identified. To address this problem, here we introduce an interval estimate as an additional measure, namely prediction interval (PI), which can be estimated from the error distribution of the predictions. The benefits of using the interval estimate are 1) it provides the uncertainty range in the predicted activities, which is important in prospective applications; 2) a true null model with well-defined PI can be established. We provide one such example termed Gaussian Random Affinity Model (GRAM), which is based on the empirical observation that the Affinity change in a typical lead optimization effort has the tendency to distribute normally N (0, s). Having an analytically defined PI that only depends on the variation in the activities, GRAM should in principle allow us to compare the performance of Relative Binding Affinity prediction methods in a standard way, ultimately critical to measuring the progress made in algorithm development.<br>
Gérard Jaouen - One of the best experts on this subject based on the ideXlab platform.
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New Synthetic Pathways of cis- or trans-Hydroxytamoxifen Derivatives with in Situ Formed [Cp*Rh(solvent)3]2+ Complexes: Kinetic and Thermodynamic Control, Including a Novel, Intramolecular N-π Rearrangement, and Relative Binding Affinities of the η6
Organometallics, 2006Co-Authors: Siden Top, Anne Vessières, Gérard Jaouen, Richard H. FishAbstract:Reactions of hydroxytamoxifen derivatives, 1 and 2, with [Cp*Rh(solvent)3]2+ complexes (solvent = MeOH or CH2Cl2) initially provided the kinetically controlled η1-N complexes, 4 and 5, which undergo a novel intramolercular N-π rearrangement to give the regioselective η6 products, 6 and 7; molecular mechanics calculations showed a preferred ground state for both the η1-N and η6 complexes, while 6 had a higher Relative Binding Affinity for the estrogen receptor, in competition with estradiol, than 7.
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Selective estrogen receptor modulators in the ruthenocene series. Synthesis and biological behavior
Journal of Medicinal Chemistry, 2005Co-Authors: Pascal Pigeon, Siden Top, Anne Vessières, Michel Huché, Elisabeth A. Hillard, Emmanuel Salomon, Gérard JaouenAbstract:A series of ruthenocene derivatives, 1-[4-(O(CH2)nN(CH3)2)phenyl]-1-(4-hydroxyphenyl)-2-ruthenocenylbut-1-ene, with n = 2-5, based on the structure of the breast cancer drug tamoxifen has been prepared. These compounds were obtained, via a McMurry cross-coupling reaction, as a mixture of Z and E isomers that could not be separated by HPLC. The Relative Binding Affinity values for estrogen receptor α (ERα) for n = 2 and 3 were very high (85 and 53%) and surpassed even that of hydroxytamoxifen (38.5%), the active metabolite of tamoxifen. Ruthenocene derivatives act as anti-estrogens as effective (n = 2) or slightly more effective (n = 3-5) than hydroxytamoxifen on ERα-positive breast cancer cell lines but, unlike ferrocifens, do not show antiproliferative effects on ERα-negative breast cancer cell lines. Electrochemical studies showed that the ruthenocifen radical cations are unstable, which may account for this behavior. Some of these compounds could be useful as radiopharmaceuticals for ERα-positive breast cancer tumors.
Elton José Ferreira Chaves - One of the best experts on this subject based on the ideXlab platform.
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Determining the Relative Binding Affinity of Ricin Toxin A Inhibitors by Using Molecular Docking and Nonequilibrium Work
Journal of chemical information and modeling, 2018Co-Authors: Elton José Ferreira Chaves, Itácio Q. M. Padilha, Demetrius A. M. Araújo, Gerd B. RochaAbstract:Ricin is a ribosome-inactivating protein (RIP type 2) consisting of two subunits, ricin toxin A (RTA) and ricin toxin B (RTB). Because of its cytotoxicity, ricin has worried world authorities for its potential use as a chemical weapon; therefore, its inhibition is of great biotechnological interest. RTA is the target for inhibitor synthesis, and pterin derivatives are promising candidates to inhibit it. In this study, we used a combination of the molecular docking approach and fast steered molecular dynamics (SMD) to assess the correlation between nonequilibrium work, ⟨W⟩, and the IC50 for six RTA inhibitors. The results showed that molecular docking is a powerful tool to predict good bioactive poses of RTA inhibitors, and ⟨W⟩ presented a strong correlation with IC50 (R2 = 0.961). Such a profile ranked the RTA inhibitors better than the molecular docking approach. Therefore, the combination of docking and fast SMD simulation was shown to be a promising tool to distinguish RTA-active inhibitors from inacti...
Guanglei Cui - One of the best experts on this subject based on the ideXlab platform.
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GRAM: A True Null Model for Relative Binding Affinity Predictions.
Journal of chemical information and modeling, 2020Co-Authors: Guanglei Cui, Alan P. Graves, Eric S. ManasAbstract:Relative Binding Affinity prediction is a critical component in computer aided drug design. A significant amount of effort has been dedicated to developing rapid and reliable in silico methods. However, robust assessment of their performance is still a complicated issue, as it requires a performance measure applicable in the prospective setting and more importantly a true null model that defines the expected performance of being random in an objective manner. Although many performance metrics, such as the Pearson correlation coefficient (r), mean unsigned error (MUE), and root-mean-square error (RMSE), are frequently used in the literature, a true and nontrivial null model has yet been identified. To address this problem, here we introduce an interval estimate as an additional measure, namely, the prediction interval (PI), which can be estimated from the error distribution of the predictions. The benefits of using the interval estimate are (1) it provides the uncertainty range in the predicted activities, which is important in prospective applications, and (2) a true null model with well-defined PI can be established. We provide one such example termed the Gaussian Random Affinity Model (GRAM), which is based on the empirical observation that the Affinity change in a typical lead optimization effort has the tendency to distribute normally N (0, σ). Having an analytically defined PI that only depends on the variation in the activities, GRAM should, in principle, allow us to compare the performance of Relative Binding Affinity prediction methods in a standard way, ultimately critical to measuring the progress made in algorithm development.
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GRAM: A True Null Model for Relative Binding Affinity Predictions
2019Co-Authors: Guanglei Cui, Alan P. Graves, Eric S. ManasAbstract:Relative Binding Affinity prediction is a critical component in computer aided drug design. Significant amount of effort has been dedicated to developing rapid and reliable in silico methods. However, robust assessment of their performance is still a complicated issue, as it requires a performance measure applicable in the prospective setting and more importantly a true null model that defines the expected performance of random in an objective manner. Although many performance metrics, such as correlation coefficient (r2), mean unsigned error (MUE), and room mean square error (RMSE), are frequently used in the literature, a true and non-trivial null model has yet been identified. To address this problem, here we introduce an interval estimate as an additional measure, namely prediction interval (PI), which can be estimated from the error distribution of the predictions. The benefits of using the interval estimate are 1) it provides the uncertainty range in the predicted activities, which is important in prospective applications; 2) a true null model with well-defined PI can be established. We provide one such example termed Gaussian Random Affinity Model (GRAM), which is based on the empirical observation that the Affinity change in a typical lead optimization effort has the tendency to distribute normally N (0, s). Having an analytically defined PI that only depends on the variation in the activities, GRAM should in principle allow us to compare the performance of Relative Binding Affinity prediction methods in a standard way, ultimately critical to measuring the progress made in algorithm development.<br>