Background: Tuberculosis is a contagious disease caused by Mycobacterium tuberculosis (Mtb), affe... more Background: Tuberculosis is a contagious disease caused by Mycobacterium tuberculosis (Mtb), affecting more than two billion people around the globe and is one of the major causes of morbidity and mortality in the developing world. Recent reports suggest that Mtb has been developing resistance to the widely used anti-tubercular drugs resulting in the emergence and spread of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains throughout the world. In view of this global epidemic, there is an urgent need to facilitate fast and efficient lead identification methodologies. Target based screening of large compound libraries has been widely used as a fast and efficient approach for lead identification, but is restricted by the knowledge about the target structure. Whole organism screens on the other hand are target-agnostic and have been now widely employed as an alternative for lead identification but they are limited by the time and cost involved in running the screens for large compound libraries. This could be possibly be circumvented by using computational approaches to prioritize molecules for screening programmes.
Novel 4-(amino aryl)methylene-2-aryl-2-imidazolin-5-ones were synthesised by a tandem reaction be... more Novel 4-(amino aryl)methylene-2-aryl-2-imidazolin-5-ones were synthesised by a tandem reaction between imidic acid ester and glycine ester in presence of a base. Computational analysis was carried out to assess the druggability of the new molecules. In vitro anticancer screening of the aminoimidazolinones and one of their trichlorides were carried out using eight cancer cell lines of various origins. The results successfully establish the synthesised aminoimidazolinone derivatives as novel compounds with pronounced anticancer activity and druglike properties.
Background: Experimental screening of chemical compounds for biological activity is a time consum... more Background: Experimental screening of chemical compounds for biological activity is a time consuming and expensive practice. In silico predictive models permit inexpensive, rapid "virtual screening" to prioritize selection of compounds for experimental testing. Both experimental and in silico screening can be used to test compounds for desirable or undesirable properties. Prior work on prediction of mutagenicity has primarily involved identification of toxicophores rather than whole-molecule predictive models. In this work, we examined a range of in silico predictive classification models for prediction of mutagenic properties of compounds, including methods such as J48 and SMO which have not previously been widely applied in cheminformatics. Results: The Bursi mutagenicity data set containing 4337 compounds (Set 1) and a Benchmark data set of 6512 compounds (Set 2) were taken as input data set in this work. A third data set (Set 3) was prepared by joining up the previous two sets. Classification algorithms including Naïve Bayes, Random Forest, J48 and SMO with 10 fold cross-validation and default parameters were used for model generation on these data sets. Models built using the combined performed better than those developed from the Benchmark data set. Significantly, Random Forest outperformed other classifiers for all the data sets, especially for Set 3 with 89.27% accuracy, 89% precision and ROC of 95.3%. To validate the developed models two external data sets, AID1189 and AID1194, with mutagenicity data were tested showing 62% accuracy with 67% precision and 65% ROC area and 91% accuracy, 91% precision with 96.3% ROC area respectively. A Random Forest model was used on approved drugs from DrugBank and metabolites from the Zinc Database with True Positives rate almost 85% showing the robustness of the model.
The aim of the work was to understand the toxicity, physically significant descriptors and pharma... more The aim of the work was to understand the toxicity, physically significant descriptors and pharmaceutically relevant properties of some imidazoles obtained from the open sources that may found to be active against tuberculosis. At present five azoles were modeled for the prediction and calculation of descriptors that were carried out by means of computational approach [1].
... UC Abdul Jaleel,1 M. Rakhila,1 and Geetha Parameswaran2 1 Cheminformatics division, Malabar C... more ... UC Abdul Jaleel,1 M. Rakhila,1 and Geetha Parameswaran2 1 Cheminformatics division, Malabar Christian College, Calicut 673001, India 2 Department of Chemistry, University of Calicut, India Correspondence should be addressed to UC Abdul Jaleel, jaleel.uc@gmail.com ...
Background: Tuberculosis is a contagious disease caused by Mycobacterium tuberculosis (Mtb), affe... more Background: Tuberculosis is a contagious disease caused by Mycobacterium tuberculosis (Mtb), affecting more than two billion people around the globe and is one of the major causes of morbidity and mortality in the developing world. Recent reports suggest that Mtb has been developing resistance to the widely used anti-tubercular drugs resulting in the emergence and spread of multi drug-resistant (MDR) and extensively drug-resistant (XDR) strains throughout the world. In view of this global epidemic, there is an urgent need to facilitate fast and efficient lead identification methodologies. Target based screening of large compound libraries has been widely used as a fast and efficient approach for lead identification, but is restricted by the knowledge about the target structure. Whole organism screens on the other hand are target-agnostic and have been now widely employed as an alternative for lead identification but they are limited by the time and cost involved in running the screens for large compound libraries. This could be possibly be circumvented by using computational approaches to prioritize molecules for screening programmes.
Novel 4-(amino aryl)methylene-2-aryl-2-imidazolin-5-ones were synthesised by a tandem reaction be... more Novel 4-(amino aryl)methylene-2-aryl-2-imidazolin-5-ones were synthesised by a tandem reaction between imidic acid ester and glycine ester in presence of a base. Computational analysis was carried out to assess the druggability of the new molecules. In vitro anticancer screening of the aminoimidazolinones and one of their trichlorides were carried out using eight cancer cell lines of various origins. The results successfully establish the synthesised aminoimidazolinone derivatives as novel compounds with pronounced anticancer activity and druglike properties.
Background: Experimental screening of chemical compounds for biological activity is a time consum... more Background: Experimental screening of chemical compounds for biological activity is a time consuming and expensive practice. In silico predictive models permit inexpensive, rapid "virtual screening" to prioritize selection of compounds for experimental testing. Both experimental and in silico screening can be used to test compounds for desirable or undesirable properties. Prior work on prediction of mutagenicity has primarily involved identification of toxicophores rather than whole-molecule predictive models. In this work, we examined a range of in silico predictive classification models for prediction of mutagenic properties of compounds, including methods such as J48 and SMO which have not previously been widely applied in cheminformatics. Results: The Bursi mutagenicity data set containing 4337 compounds (Set 1) and a Benchmark data set of 6512 compounds (Set 2) were taken as input data set in this work. A third data set (Set 3) was prepared by joining up the previous two sets. Classification algorithms including Naïve Bayes, Random Forest, J48 and SMO with 10 fold cross-validation and default parameters were used for model generation on these data sets. Models built using the combined performed better than those developed from the Benchmark data set. Significantly, Random Forest outperformed other classifiers for all the data sets, especially for Set 3 with 89.27% accuracy, 89% precision and ROC of 95.3%. To validate the developed models two external data sets, AID1189 and AID1194, with mutagenicity data were tested showing 62% accuracy with 67% precision and 65% ROC area and 91% accuracy, 91% precision with 96.3% ROC area respectively. A Random Forest model was used on approved drugs from DrugBank and metabolites from the Zinc Database with True Positives rate almost 85% showing the robustness of the model.
The aim of the work was to understand the toxicity, physically significant descriptors and pharma... more The aim of the work was to understand the toxicity, physically significant descriptors and pharmaceutically relevant properties of some imidazoles obtained from the open sources that may found to be active against tuberculosis. At present five azoles were modeled for the prediction and calculation of descriptors that were carried out by means of computational approach [1].
... UC Abdul Jaleel,1 M. Rakhila,1 and Geetha Parameswaran2 1 Cheminformatics division, Malabar C... more ... UC Abdul Jaleel,1 M. Rakhila,1 and Geetha Parameswaran2 1 Cheminformatics division, Malabar Christian College, Calicut 673001, India 2 Department of Chemistry, University of Calicut, India Correspondence should be addressed to UC Abdul Jaleel, jaleel.uc@gmail.com ...
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