Following the explosive growth in chemical and biological data, the shift from traditional methods of drug discovery to computer-aided means has made data mining and machine learning methods integral parts of today's drug discovery process. In this paper, extreme gradient boosting (Xgboost), which is an ensemble of Classification and Regression Tree (CART) and a variant of the Gradient Boosting Machine, was investigated for the prediction of biological activity based on quantitative description of the compound's molecular structure. Seven datasets, well known in the literature were used in this paper and experimental results show that Xgboost can outperform machine learning algorithms like Random Forest (RF), Support Vector Machines (LSVM), Radial Basis Function Neural Network (RBFN) and Naive Bayes (NB) for the prediction of biological activities. In addition to its ability to detect minority activity classes in highly imbalanced datasets, it showed remarkable performance on both high and low diversity datasets.

/pdf/bioactive-molecule-prediction-using-extreme-gradient-28bxt6q7rb.pdf

Bioactive molecule prediction using extreme gradient boosting

Comprehensive Medicinal Chemistry III

In the past few years, retail market industries have taken a broad form to sell the products online and also to give the opportunity to customers to provide their valuable feedbacks, suggestions and recommendations. The aim of this paper is to provide an automatic comment analyzer. And propose an automatic comment analyzer and classification system which can determine the polarity of the customer comments collected from Amazon and Flipkart data domains effectively. This system should be able to process the large number of reviews. It should categorize the comments as positive, negative and neutral classes using five prime supervised learning classifiers such as NB, LR, SentiWordNet, RF and KNN. The paper also discusses their experimental results and challenges found. Therefore, this study shows the maximum usage of feature extraction, positive–negative sentiment, Amazon web source, mobile phone for a large set of reviews in the existing algorithms. It included the preliminary definitions, information extraction and retrieval aspects, role of machine learning, and the comment mining. The comment analysis and classification described comment polarity, orientation, subjectivity detection, comment analysis, summarization and classification. The classification of comment analysis techniques explained various lexicons and supervised algorithms along with the essential used-based concerns.

Sentiment Analysis of Amazon Product Reviews Using Hybrid Rule-Based Approach

The Sentiment analysis from text documents is emerging field for the research in Natural Language Processing (NLP) and text mining. Feature specific opinion matters more than the overall opinion. Given a collection of review texts, the goal is to detect the individual product aspects comments by reviewers and to decide whether the comments are rather positive or negative. In this research paper unsupervised approach for domain independent feature specific sentiment analysis has been proposed. SentiWordNet lexical resource has been used to determine the polarity of identified features. Research work has shown the promising results over the previously used approaches using SentiWordNet. Newly introduced SentiWordNet 3.0 has been proved to be an important lexical resource.

Domain Independent Approach for Aspect Oriented Sentiment Analysis for Product Reviews

Quantifying the similarity of molecules is considered one of the major tasks in virtual screening. There are many similarity measures that have been proposed for this purpose, some of which have been derived from document and text retrieving areas as most often these similarity methods give good results in document retrieval and can achieve good results in virtual screening. In this work, we propose a similarity measure for ligand-based virtual screening, which has been derived from a text processing similarity measure. It has been adopted to be suitable for virtual screening; we called this proposed measure the Adapted Similarity Measure of Text Processing (ASMTP). For evaluating and testing the proposed ASMTP we conducted several experiments on two different benchmark datasets: the Maximum Unbiased Validation (MUV) and the MDL Drug Data Report (MDDR). The experiments have been conducted by choosing 10 reference structures from each class randomly as queries and evaluate them in the recall of cut-offs at 1% and 5%. The overall obtained results are compared with some similarity methods including the Tanimoto coefficient, which are considered to be the conventional and standard similarity coefficients for fingerprint-based similarity calculations. The achieved results show that the performance of ligand-based virtual screening is better and outperforms the Tanimoto coefficients and other methods.

/pdf/adapting-document-similarity-measures-for-ligand-based-3gifnbx6b0.pdf

Adapting Document Similarity Measures for Ligand-Based Virtual Screening

One of the most widely-used techniques for ligand-based virtual screening is similarity searching. This study adopted the concepts of quantum mechanics to present as state-of-the-art similarity method of molecules inspired from quantum theory. The representation of molecular compounds in mathematical quantum space plays a vital role in the development of quantum-based similarity approach. One of the key concepts of quantum theory is the use of complex numbers. Hence, this study proposed three various techniques to embed and to re-represent the molecular compounds to correspond with complex numbers format. The quantum-based similarity method that developed in this study depending on complex pure Hilbert space of molecules called Standard Quantum-Based (SQB). The recall of retrieved active molecules were at top 1% and top 5%, and significant test is used to evaluate our proposed methods. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints. Simulated virtual screening experiment show that the effectiveness of SQB method was significantly increased due to the role of representational power of molecular compounds in complex numbers forms compared to Tanimoto benchmark similarity measure.

/pdf/a-quantum-based-similarity-method-in-virtual-screening-2c8df8oc0x.pdf

A quantum-based similarity method in virtual screening

There is no doubt that the process of using the internet to post comments and to get others’ comments has become a common daily practice on the Web. Nowadays, a huge amount of information is available on the internet. The data which is posted by users and customers who visit these websites every day contain significant information. Some companies ask their customers about a product or services, for feedback analysis and to evaluate the satisfaction ratio of their products and services. The reviews by customers of products are rapidly growing. This paper provides ground knowledge and covers the most important scholarly papers and research that have been done in the area of sentiment analysis and the classification of opinion. This work presents opinion definitions and more detailed opinion classifications, and explains the related topics. This review will provide an introduction to the most common and significant information related to sentiment analysis, and it will answer many questions that have been asked in opinion mining, analysis, classifications and challenges.

Survey on Product Review Sentiment Classification and Analysis Challenges

The volume of electronic documents has rapidly increased and the scientific literature has increased too. These huge documents contain considerable information, but it has to be retrieved and managed in a constructive and useful way. Information Extraction (IE) is the field of extracting useful information using different methods and approaches by means of Natural Language Processing (NLP). Researchers still continue to try to identify proper methods to extract information from texts, such as opinions on the internet, medical data, clinical reports, medical reports, notes, papers, patents, etc. Recently a new trend to expand working in IE is taking place by enriching the extraction process to include the extraction of information from images and videos. In this paper, the classification of IE tasks is discussed, as well as the proposed methods and techniques of IE from chemical documents. A more focused approach is then taken into consideration regarding biomedical language processing and ontology. Finally, the paper discusses some of the challenges that the IE field is facing.

Information extraction methods and extraction techniques in the chemical document's contents: survey

Computational methods in drug discovery have increasingly gained attention of researchers within the last decades, as the tools for facilitating drug discovery process, for it allow rapid screening of huge databases , the ligand-abased virtual screening (LBVS) methods continue to be developed and improved as one of the important tool of drug discovery process ,and it is become one of the most interested area in Chemo informatics, this review discuss various methods that are applied for LBVS ,the paper focus deeply in the various of machine learning techniques that have been applied for LBVS , and also it will demonstrate and discuss the effectiveness of data fusion in LBVS ,and how these techniques provided effectiveness tools could enhance the LBVS .

Data mining and fusion methods in ligand-based virtual screening

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