Multi-target QSAR modelling in the analysis and design of HIV-HCV co-inhibitors: an in-silico study

Abstract

BackgroundHIV and HCV infections have become the leading global public-health threats. Even more remarkable, HIV-HCV co-infection is rapidly emerging as a major cause of morbidity and mortality throughout the world, due to the common rapid mutation characteristics of the two viruses as well as their similar complex influence to immunology system. Although considerable progresses have been made on the study of the infection of HIV and HCV respectively, few researches have been conducted on the investigation of the molecular mechanism of their co-infection and designing of the multi-target co-inhibitors for the two viruses simultaneously.ResultsIn our study, a multi-target Quantitative Structure-Activity Relationship (QSAR) study of the inhibitors for HIV-HCV co-infection were addressed with an in-silico machine learning technique, i.e. multi-task learning, to help to guide the co-inhibitor design. Firstly, an integrated dataset with 3 HIV inhibitor subsets targeted on protease, integrase and reverse transcriptase respectively, together with another 6 subsets of 2 HCV inhibitors targeted on NS3 serine protease and NS5B polymerase respectively were compiled. Secondly, an efficient multi-target QSAR modelling of HIV-HCV co-inhibitors was performed by applying an accelerated gradient method based multi-task learning on the whole 9 datasets. Furthermore, by solving the L-1-infinity regularized optimization, the Drug-like index features for compound description were ranked according to their joint importance in multi-target QSAR modelling of HIV and HCV. Finally, a drug structure-activity simulation for investigating the relationships between compound structures and binding affinities was presented based on our multiple target analysis, which is then providing several novel clues for the design of multi-target HIV-HCV co-inhibitors with increasing likelihood of successful therapies on HIV, HCV and HIV-HCV co-infection.ConclusionsThe framework presented in our study provided an efficient way to identify and design inhibitors that simultaneously and selectively bind to multiple targets from multiple viruses with high affinity, and will definitely shed new lights on the future work of inhibitor synthesis for multi-target HIV, HCV, and HIV-HCV co-infection treatments.

Integrated QSAR study for inhibitors of hedgehog signal pathway against multiple cell lines:a collaborative filtering method

“…The domain of application (DOA) is used to estimate the reliability in the prediction of a new compound [24] for a specific method. Those molecules fall out the domain may lead to unreliable predictions [10]. In the analysis of DOA, a value of leverage $h_i$ is defined in equation (11) for each chemical molecule:…”

“…Based on the definition of leverage, Williams plot was used in this study to visualize the DOA of the QSAR model [10]. The Williams plot plots the standardized cross-validated residuals (RES) versus leverage values ( h ), and can be used to obtain an immediate and simple graphical detection of both the response outliers ( Y outliers) and the structurally influential chemicals ( X outliers) of a model.…”

“…The Williams plot plots the standardized cross-validated residuals (RES) versus leverage values ( h ), and can be used to obtain an immediate and simple graphical detection of both the response outliers ( Y outliers) and the structurally influential chemicals ( X outliers) of a model. Generally, the points with their values of Y axis fall outside the 3 σ line ( σ is the standard residuals unit of the compounds) can be considered as Y outliers, while the points with their values of X axis fall outside the warning leverage $h*$line can be considered as X outliers [10]. Figures 8 and 9 represent the William plots for the four cell lines with compound representations of General Descriptor and Drug-like index respectively.…”

“…Although the CMF based QSAR modeling was investigated in our study, we do realize the existence of other QSAR modeling with integrated information, and we call such models as the “collaborative” QSAR modeling, like the neural network based [15][25-27] and multi-task learning based [9][10] models, as well as the proteochemometrics modeling (PCM) [28][29]. In order to further uncover the characteristics of such collaborative QSAR modeling, we discuss our CMF based method with the aforementioned methods on our multiple cell line QSAR modeling for the inhibitors of Hedgehog Signaling Pathway.…”

“…), and will provide novel insights on the integration of existing information, avoiding repeatable laborious work in drug discovery. In addition, such a study may also lead to novel computational models for integrated QSAR modeling, which is closely related to multi-task QSAR modeling [9][10], Multi-Assay-Based QSAR modeling [11] , and Multi-target QSAR study [12]. …”