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

Fast and accurate identification of inhibitors with potency against HCV NS5B polymerase is currently a challenging task. As conventional experimental methods is the gold standard method for the design and development of new HCV inhibitors, they often require costly investment of time and resources. In this study, we develop a novel machine learning-based meta-predictor (termed StackHCV) for accurate and large-scale identification of HCV inhibitors. Unlike the existing method, which is based on single-feature-based approach, we first constructed a pool of various baseline models by employing a wide range of heterogeneous molecular fingerprints with five popular machine learning algorithms (k-nearest neighbor, multi-layer perceptron, partial least squares, random forest and support vectors machine). Secondly, we integrated these baseline models in order to develop the final meta-based model by means of the stacking strategy. Extensive benchmarking experiments showed that StackHCV achieved a more accurate and stable performance as compared to its constituent baseline models on the training dataset and also outperformed the existing predictor on the independent test dataset. To facilitate the high-throughput identification of HCV inhibitors, we built a web server that can be freely accessed at http://camt.pythonanywhere.com/StackHCV . It is expected that StackHCV could be a useful tool for fast and precise identification of potential drugs against HCV NS5B particularly for liver cancer therapy and other clinical applications.

QSAR-driven rational design of novel DNA methyltransferase 1 inhibitors.

DNA methylation, an epigenetic modification, is mediated by DNA methyltransferases (DNMTs), a family of enzymes. Inhibitions of these enzymes are considered a promising strategy for the treatment of several diseases. In this study, a quantitative structure-activity relationship (QSAR) modeling was employed to understand the structure-activity relationship (SAR) of currently available non-nucleoside DNMT1 inhibitors (i.e., indole and oxazoline/1,2-oxazole scaffolds). Two QSAR models were successfully constructed using multiple linear regression (MLR) and provided good predictive performance (R2 Tr = 0.850-0.988 and R2 CV = 0.672-0.869). Bond information content index (BIC1) and electronegativity (R6e+) are the most influential descriptors governing the activity of compounds. The constructed QSAR models were further applied for guiding a rational design of novel inhibitors. A novel set of 153 structurally modified compounds were designed in silico according to the important descriptors deduced from the QSAR finding, and their DNMT1 inhibitory activities were predicted. This result demonstrated that 86 newly designed inhibitors were predicted to elicit enhanced DNMT1 inhibitory activity when compared to their parent compounds. Finally, a set of promising compounds as potent DNMT1 inhibitors were highlighted to be further developed. The key SAR findings may also be beneficial for structural optimization to improve properties of the known inhibitors.

HCVpred: A web server for predicting the bioactivity of hepatitis C virus NS5B inhibitors.

Hepatitis C virus (HCV) is one of the major causes of liver disease affecting an estimated 170 million people culminating in 300,000 deaths from cirrhosis or liver cancer. NS5B is one of three potential therapeutic targets against HCV (i.e., the other two being NS3/4A and NS5A) that is central to viral replication. In this study, we developed a classification structure-activity relationship (CSAR) model for identifying substructures giving rise to anti-HCV activities among a set of 578 non-redundant compounds. NS5B inhibitors were described by a set of 12 fingerprint descriptors and predictive models were constructed from 100 independent data splits using the random forest algorithm. The modelability (MODI index) of the data set was determined to be robust with a value of 0.88 exceeding established threshold of 0.65. The predictive performance was deduced by the accuracy, sensitivity, specificity, and Matthews correlation coefficient, which was found to be statistically robust (i.e., the former three parameters afforded values in excess of 0.8 while the latter statistical parameter provided a value >0.7). An in-depth analysis of the top 20 important descriptors revealed that aromatic ring and alkyl side chains are important for NS5B inhibition. Finally, the predictive model is deployed as a publicly accessible HCVpred web server (available at http://codes.bio/hcvpred/) that would allow users to predict the biological activity as being active or inactive against HCV NS5B. Thus, the knowledge and web server presented herein can be used in the design of more potent and specific drugs against the HCV NS5B.

Privileged substructures for anti-sickling activity via cheminformatic analysis.

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.

Correction: Privileged substructures for anti-sickling activity via cheminformatic analysis.

[This corrects the article DOI: 10.1039/C7RA12079F.].

Exploring the epigenetic drug discovery landscape.

INTRODUCTION: Epigenetic modification has been implicated in a wide range of diseases and the ability to modulate such systems is a lucrative therapeutic strategy in drug discovery. Areas covered: This article focuses on the concepts and drug discovery aspects of epigenomics. This is achieved by providing a survey of the following concepts: (i) factors influencing epigenetics, (ii) diseases arising from epigenetics, (iii) epigenetic enzymes as druggable targets along with coverage of existing FDA-approved drugs and pharmacological agents, and (iv) drug repurposing/repositioning as a means for rapid discovery of pharmacological agents targeting epigenetics. Expert opinion: Despite significant interests in targeting epigenetic modifiers as a therapeutic route, certain classes of target proteins are heavily studied while some are less characterized. Thus, such orphan target proteins are not yet druggable with limited report of active modulators. Current research points towards a great future with novel drugs directed to the many complex multifactorial diseases of humans, which are still often poorly understood and difficult to treat.