Investigation of solvent effect and NMR shielding tensors of p53 tumor-suppressor gene in drug design

The p53 tumor-suppressor gene encodes a nuclear phosphoprotein with cancer- inhibiting properties. The most probable cancerous mutations occur as point mutations in exons 5 up to 8 of p53, as a base pair substitution that encompasses CUA and GAT sequences. As DNA drug design represents a direct genetic treatment of cancer, in the research reported computational drug design was carried out to explore, at the Hartree–Fock level, effects of solvents on the thermochemical properties and nuclear magnetic resonance (NMR) shielding tensors of some atoms of CUA involved in the hydrogen-bonding network. The observed NMR shielding variations of the solutes caused by solvent change seemed significant and were attributed to solvent polarity, and solute–solvent and solvent–solute hydrogen-bonding interactions. The results provide a reliable insight into the nature of mutation processes. However, to improve our knowledge of the hydration pattern more rigorous computations of the hydrated complexes are needed

QSAR models and scaffold-based analysis of non-nucleoside HIV RT inhibitors

© 2015 Elsevier B.V. A selection of 289 pyrimidine derivatives with anti-HIV RT activities as non-nucleoside HIV RT inhibitors (NNRTI) were studied. The associative neural network (ASNN) method was applied to develop a quantitative structure-activity relationship (QSAR) for anti-HIV RT activity. The calculated models were validated using the bagging approach. A consensus model with R2=0.87 and RMSE=0.5 was obtained from 10 individual models. Scaffold analysis and molecular docking of the compounds used in the QSAR model identified a potential chemical scaffold. The results showed that scaffold-based analysis of the QSAR model could be helpful in identifying potent scaffolds for further exploration than analyzing the overall model. Matched molecular pair analysis (MMPA) was applied in the QSAR model to characterize molecular transformations causing a significant change in the anti-HIV activity. The linear QSAR model was calculated to explore the structural features important for NNRTI activity. The results revealed that the activity of NNRT inhibitors is strongly dependent on their aromaticity and structural flexibility. The scaffold-based analysis of QSAR models with molecular docking and MMPA was found to be helpful in characterizing potential scaffolds for anti-HIV RT derivatives. The outcome of this study provides a deeper insight into the computer-aided scaffold-based design of novel molecules with HIV RT activities. It was also clearly shown that the consensus model's failure to correctly predict new chemical series could be due to the limitation of its applicability domain (AD). Redevelopment of models using new measurements can dramatically increase their AD and performance

QSAR analysis on tacrine-related acetylcholinesterase inhibitors

Background: The evaluation of the clinical effects of Tacrine has shown efficacy in delaying the deterioration of the symptoms of Alzheimer's disease, while confirming the adverse events consisting mainly in the elevated liver transaminase levels. The study of tacrine analogs presents a continuous interest, and for this reason we establish Quantitative Structure-Activity Relationships on their Acetylcholinesterase inhibitory activity. Results: Ten groups of new developed Tacrine-related inhibitors are explored, which have been experimentally measured in different biochemical conditions and AChE sources. The number of included descriptors in the structure-activity relationship is characterized by 'Rule of Thumb'. The 1502 applied molecular descriptors could provide the best linear models for the selected Alzheimer's data base and the best QSAR model is reported for the considered data sets. Conclusion: The QSAR models developed in this work have a satisfactory predictive ability, and are obtained by selecting the most representative molecular descriptors of the chemical structure, represented through more than a thousand of constitutional, topological, geometrical, quantum-mechanical and electronic descriptor types.Instituto de Investigaciones Fisicoquímicas Teóricas y Aplicada

QSAR models and scaffold-based analysis of non-nucleoside HIV RT inhibitors.

A selection of 289 pyrimidine derivatives with anti-HIV RT activities as non-nucleoside HIV RT inhibitors (NNRTI) were studied. The associative neural network (ASNN) method was applied to develop a quantitative structure-activity relationship (QSAR) for anti-HIV RT activity. The calculated models were validated using the bagging approach. A consensus model with R2=0.87 and RMSE=0.5 was obtained from 10 individual models. Scaffold analysis and molecular docking of the compounds used in the QSAR model identified a potential chemical scaffold. The results showed that scaffold-based analysis of the QSAR model could be helpful in identifying potent scaffolds for further exploration than analyzing the overall model. Matched molecular pair analysis (MMPA) was applied in the QSAR model to characterize molecular transformations causing a significant change in the anti-HIV activity. The linear QSAR model was calculated to explore the structural features important for NNRTI activity. The results revealed that the activity of NNRT inhibitors is strongly dependent on their aromaticity and structural flexibility. The scaffold-based analysis of QSAR models with molecular docking and MMPA was found to be helpful in characterizing potential scaffolds for anti-HIV RT derivatives. The outcome of this study provides a deeper insight into the computer-aided scaffold-based design of novel molecules with HIV RT activities. It was also clearly shown that the consensus model's failure to correctly predict new chemical series could be due to the limitation of its applicability domain (AD). Redevelopment of models using new measurements can dramatically increase their AD and performance

QSAR models and scaffold-based analysis of non-nucleoside HIV RT inhibitors

© 2015 Elsevier B.V. A selection of 289 pyrimidine derivatives with anti-HIV RT activities as non-nucleoside HIV RT inhibitors (NNRTI) were studied. The associative neural network (ASNN) method was applied to develop a quantitative structure-activity relationship (QSAR) for anti-HIV RT activity. The calculated models were validated using the bagging approach. A consensus model with R2=0.87 and RMSE=0.5 was obtained from 10 individual models. Scaffold analysis and molecular docking of the compounds used in the QSAR model identified a potential chemical scaffold. The results showed that scaffold-based analysis of the QSAR model could be helpful in identifying potent scaffolds for further exploration than analyzing the overall model. Matched molecular pair analysis (MMPA) was applied in the QSAR model to characterize molecular transformations causing a significant change in the anti-HIV activity. The linear QSAR model was calculated to explore the structural features important for NNRTI activity. The results revealed that the activity of NNRT inhibitors is strongly dependent on their aromaticity and structural flexibility. The scaffold-based analysis of QSAR models with molecular docking and MMPA was found to be helpful in characterizing potential scaffolds for anti-HIV RT derivatives. The outcome of this study provides a deeper insight into the computer-aided scaffold-based design of novel molecules with HIV RT activities. It was also clearly shown that the consensus model's failure to correctly predict new chemical series could be due to the limitation of its applicability domain (AD). Redevelopment of models using new measurements can dramatically increase their AD and performance

QSAR models and scaffold-based analysis of non-nucleoside HIV RT inhibitors

© 2015 Elsevier B.V. A selection of 289 pyrimidine derivatives with anti-HIV RT activities as non-nucleoside HIV RT inhibitors (NNRTI) were studied. The associative neural network (ASNN) method was applied to develop a quantitative structure-activity relationship (QSAR) for anti-HIV RT activity. The calculated models were validated using the bagging approach. A consensus model with R2=0.87 and RMSE=0.5 was obtained from 10 individual models. Scaffold analysis and molecular docking of the compounds used in the QSAR model identified a potential chemical scaffold. The results showed that scaffold-based analysis of the QSAR model could be helpful in identifying potent scaffolds for further exploration than analyzing the overall model. Matched molecular pair analysis (MMPA) was applied in the QSAR model to characterize molecular transformations causing a significant change in the anti-HIV activity. The linear QSAR model was calculated to explore the structural features important for NNRTI activity. The results revealed that the activity of NNRT inhibitors is strongly dependent on their aromaticity and structural flexibility. The scaffold-based analysis of QSAR models with molecular docking and MMPA was found to be helpful in characterizing potential scaffolds for anti-HIV RT derivatives. The outcome of this study provides a deeper insight into the computer-aided scaffold-based design of novel molecules with HIV RT activities. It was also clearly shown that the consensus model's failure to correctly predict new chemical series could be due to the limitation of its applicability domain (AD). Redevelopment of models using new measurements can dramatically increase their AD and performance

QSAR models and scaffold-based analysis of non-nucleoside HIV RT inhibitors

© 2015 Elsevier B.V. A selection of 289 pyrimidine derivatives with anti-HIV RT activities as non-nucleoside HIV RT inhibitors (NNRTI) were studied. The associative neural network (ASNN) method was applied to develop a quantitative structure-activity relationship (QSAR) for anti-HIV RT activity. The calculated models were validated using the bagging approach. A consensus model with R2=0.87 and RMSE=0.5 was obtained from 10 individual models. Scaffold analysis and molecular docking of the compounds used in the QSAR model identified a potential chemical scaffold. The results showed that scaffold-based analysis of the QSAR model could be helpful in identifying potent scaffolds for further exploration than analyzing the overall model. Matched molecular pair analysis (MMPA) was applied in the QSAR model to characterize molecular transformations causing a significant change in the anti-HIV activity. The linear QSAR model was calculated to explore the structural features important for NNRTI activity. The results revealed that the activity of NNRT inhibitors is strongly dependent on their aromaticity and structural flexibility. The scaffold-based analysis of QSAR models with molecular docking and MMPA was found to be helpful in characterizing potential scaffolds for anti-HIV RT derivatives. The outcome of this study provides a deeper insight into the computer-aided scaffold-based design of novel molecules with HIV RT activities. It was also clearly shown that the consensus model's failure to correctly predict new chemical series could be due to the limitation of its applicability domain (AD). Redevelopment of models using new measurements can dramatically increase their AD and performance