Abstract:With the exponential rise in the number of viable novel drug targets, computational methods are being increasingly applied to accelerate the drug discovery process. Virtual High Throughput Screening (vHTS) is one such established methodology to identify drug candidates from large collection of compound libraries. Although it complements the expensive and time consuming High Throughput Screening (HTS) of compound libraries, vHTS possess inherent challenges. The successful vHTS requires the careful implementation of each phase of computational screening experiment right from target preparation to hit identification and lead optimization. This article discusses some of the important considerations that are imperative for designing a successful vHTS experiment.Keywords: virtual high throughput screening; receptor based and ligand based screening; homology models; chemical databases; ADME filters; toxicity filters Background:The discovery of novel drug targets has increased exponentially in recent years due to advances in genomic and molecular biology techniques. Experimental and computational methods are effectively applied to accelerate the process of lead identification and optimization. The chemical leads are small potential drug like molecules which are capable of modulating the function of the target proteins that are further optimized to act as a therapeutic drug against a targeted disease. Conventional experimental methods like High Throughput Screening (HTS) continue to be the best method for rapid identification of drug leads. HTS identifies lead molecules by performing individual biochemical assays with over millions of compounds. However, the huge cost and time consumed with this technology has lead to the integration of cheaper and effective computational methodology namely virtual High Throughput Screening (vHTS). vHTS is a computational screening method which is widely applied to screen insilico collection of compound libraries to check the binding affinity of the target receptor with the library compounds [1]. This is achieved by using a scoring function which computes the complementarity of the target receptor with the compounds. HTS and vHTS are complementary methods [2] and vHTS has been shown to reduce false positives in HTS [3]. Several vHTS strategies have been practiced [4] and the technique is being continuously optimized for better performance.

A novel coronavirus spillover event has emerged as a pandemic affecting public health globally. S creening of large numbers of individuals is the need of the hour to curb the spread of disease in the community. Realtime PCR is a standard diagnostic tool being used for pathological testing. But the increasing number of false test results has opened the path for exploration of alternative testing tools. Chest X-Rays of COVID-19 patients have proved to be an important alternative indicator in COVID-19 screening. But again, accuracy depends upon radiological expertise. A diagnosis recommender system that can assist the doctor to examine the lung images of the patients will reduce the diagnostic burden of the doctor. Deep Learning techniques specifically Convolutional Neural Networks (CNN) have proven successful in medical imaging classification. Four different deep CNN architectures were investigated on images of chest X-Rays for diagnosis of COVID-19. These models have been pre-trained on the ImageNet database thereby reducing the need for large training sets as they have pre-trained weights. It was observed that CNN based architectures have the potential for diagnosis of COVID-19 disease.

Software testing is an important and expensive phase of the software development life cycle. Over the past few decades, there has been an ongoing research to automate the process of software testing but the attempts have been constrained by the size and the complexity of software especially due to the use of dynamic memory allocation which makes the software behavior highly unpredictable. The use of metaheuristic global search techniques for software test data generation has been the focus of researchers in recent years. Many new techniques and hybrid methods have been proposed to tackle the problem more effectively. This study provides an overview of the various techniques that have been applied for structural test data generation. It also presents the open areas, challenges and future directions in the field of search based software testing with an emphasis on test data generation for structural testing.

Online reviews are the most valuable sources of information about customer opinions and are considered the pillars on which the reputation of an organisation is built. From a customer’s perspective, review information is key to making a proper decision regarding an online purchase. Reviews are generally considered an unbiased opinion of an individual’s personal experience with a product, but the underlying truth about these reviews tells a different story. Spammers exploit these review platforms illegally because of incentives involved in writing fake reviews, thereby trying to gain an advantage over competitors resulting in an explosive growth of opinion spamming. The present study analyses and categorises the available literature on opinion spamming according to three detection targets: (1) opinion spam, (2) opinion spammers, and (3) collusive opinion spammer groups. The study further highlights and divides opinion spamming into three types based on textual and linguistic, behavioural, and relational features. Moreover, several state-of-the-art machine-learning techniques for opinion spam detection have also been discussed in the study. It concludes with a summary of the research articles on opinion spam detection and some interesting results to assist researchers for further exploration of the domain.

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