Abstract
To discover new inhibitors against the human colon carcinoma HCT116 cell line, two quantitative structure–activity relationship (QSAR) studies using molecular and nuclear magnetic resonance (NMR) descriptors were developed through exploration of machine learning techniques and using the value of half maximal inhibitory concentration (IC50). In the first approach, A, regression models were developed using a total of 7339 molecules that were extracted from the ChEMBL and ZINC databases and recent literature. The performance of the regression models was successfully evaluated by internal and external validations, the best model achieved R2 of 0.75 and 0.73 and root mean square error (RMSE) of 0.66 and 0.69 for the training and test sets, respectively. With the inherent time-consuming efforts of working with natural products (NPs), we conceived a new NP drug hit discovery strategy that consists in frontloading samples with 1D NMR descriptors to predict compounds with anticancer activity prior to bioactivity screening for NPs discovery, approach B. The NMR QSAR classification models were built using 1D NMR data (1H and 13C) as descriptors, from 50 crude extracts, 55 fractions and five pure compounds obtained from actinobacteria isolated from marine sediments collected off the Madeira Archipelago. The overall predictability accuracies of the best model exceeded 63% for both training and test sets.
Highlights
Colorectal cancer is the third most commonly detected cancer and the fourth foremost cause of cancer deaths in the world, accounting for about 1.4 million new cases and almost 700,000 deaths in2012 [1,2]
The results suggest that the chemoinformatics quantitative structure–activity relationship (QSAR) approach relying on a ligand-based methodology either based on the molecular structures or the nuclear magnetic resonance (NMR) spectra, corroborated with an experimental approach and could be used to predict new compounds inhibitors against the human colon carcinoma HCT116 cell line
The QSAR regression model developed here, Approach A, is the largest study ever performed with regard both to the number of compounds involved and to the number of structural families involved in the modeling of inhibitory activity against
Summary
Colorectal cancer is the third most commonly detected cancer and the fourth foremost cause of cancer deaths in the world, accounting for about 1.4 million new cases and almost 700,000 deaths in2012 [1,2]. Colorectal cancer is the third most commonly detected cancer and the fourth foremost cause of cancer deaths in the world, accounting for about 1.4 million new cases and almost 700,000 deaths in. The distribution of colorectal cancer burden varies widely, with more than two-thirds of all cases and almost 50% of all deaths occurring in more developed regions [2]. Several new methodologies have been developed and applied in drug R&D to shorten the research cycle and to reduce costs. Computer-aided drug design (CADD) methods have emerged as powerful tools in the development of therapeutically important small molecules for over three decades, with higher hit rates than that of the high throughput screening (HTS) approaches [6,7,8]
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