Privileged substructures for anti-sickling activity via cheminformatic analysis

Abstract

Sickle cell disease (SCD), an autosomal recessive genetic disorder, has been recognized by the World Health Organization (WHO) as a major public health problem as it affects 300 000 individuals worldwide.Complications arising from SCD include anemia, microvascular occlusion, severe pain, stokes, renal dysfunction and infections. A lucrative therapeutic strategy is to employ anti-sickling agents that can disrupt the formation of the HbS polymer. This study therefore employed cheminformatic approaches, encompassing classification structure-activity relationship (CSAR) modeling, to deduce the privileged substructures giving rise to the anti-sickling activity of an investigated set of 115 compounds, followed by substructure analysis. Briefly, the compiled compounds were described by fingerprint descriptors and used in the construction of CSAR models via several machine learning algorithms. The modelability of the data set, as exemplified by the MODI index, was determined to be in the range of 0.70-0.84. The predictive performance was deduced by the accuracy, sensitivity, specificity and Matthews correlation coefficient, which was found to be statistically robust, whereby the former three parameters afforded values in excess of 0.7 while the latter statistical parameter provided a value greater than 0.5. An analysis of the top 20 important substructure descriptors for anti-sickling activity revealed that 10 important features were significant in the differentiation of actives from inactives, as illustrated by aromaticity/ conjugation (e.g. SubFPC287, SubFPC171 and SubFPC5), carbonyl groups (e.g. SubFPC137, SubFPC139, SubFPC49 and SubFPC135) and miscellaneous groups (e.g. SubFPC303, SubFPC302 and SubFPC275).Furthermore, an analysis of the structure-activity relationship revealed that the length of alkyl chains, choice of functional moiety and position of substitution on the benzene ring may affect the anti-sickling activity of these compounds. Thus, this knowledge is anticipated to be useful for guiding the design of robust compounds against the gelling activity of HbS, as preliminarily demonstrated in the data-driven compound design presented herein.

Machine learning‐guided design of potent darunavir analogs targeting HIV‐1 proteases: A computational approach for antiretroviral drug discovery

“…To gain further insights, we conducted additional analysis on our dataset to observe the percentage of active and inactive compounds containing each important substructure (Figure 3B). This analysis helped clarify which substructures corresponded to the bioactivity or bio‐inactivity of compounds 55 . The informative substructures with high percentages of active compounds would be employed in designing DRV analogs since they have demonstrated inhibitory activity across multiple compounds.…”

“…This analysis helped clarify which substructures corresponded to the bioactivity or bio-inactivity of compounds. 55 The informative substructures with high percentages of active compounds would be employed in designing DRV analogs since they have demonstrated inhibitory activity across multiple compounds.…”

StackHCV: a web-based integrative machine-learning framework for large-scale identification of hepatitis C virus NS5B inhibitors

“…The utility and usefulness of this technique have been described by various applications in computational biology research [26,31]. In the first step, the RF method was used to estimate the feature importance score based on the mean decrease of the Gini index (MDGI) [15,32,33]. Here, 10 different RF models were constructed by varying the mtry parameter settings from 2 to 20 (mtry = 2, 3,5,7,9,11,13,15,17,20) and fixing the ntree parameter with 100 based on a 10-fold cross-validation procedure in order to enhance the reliability for determining the feature importance [34][35][36][37][38].…”

Detection of new candidate compounds against four antibiotic targets using explainable artificial intelligence by molecular fingerprints

“…SubFPC137 are fingerprints called substructure fingerprint count. This fingerprint shows the number of vinylogen ester groups in the molecule [41]. The vinylogen ester group is a functional group with a carbonyl group attached to an alkene.…”