Chemical Information Bulletin

Periodic supplement for "the regular journals of the American Chemical Society," containing annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the American Chemical Society (ACS) Division of Chemical Literature

Chemical Information Bulletin

Periodic supplement for "the regular journals of the American Chemical Society," containing annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the American Chemical Society (ACS) Division of Chemical Literature

Delving into dengue virus drug discovery- insights into the structural characteristics of the RNA-dependent RNA polymerase.

Masters Degrees (Pharmaceutical Sciences). University of KwaZulu-Natal. Westville, 2017.A precipitous increase in the number of flaviviral infections has been noted over the last five years. The present study sought to investigate a notorious flavivirus that has been in circulation for over 30 years. Over the last few decades, DENV has re-emerged in various serotypes and is causing mayhem in the lives of many. Dengue is dreaded for the severe fever it causes in its advanced stage. Dengue has the reputation of what is known as Dengue haemorrhagic fever (DHF) and dengue shock syndrome (DSS). Dengue remains an unmet medical need that demands prompt attention. There remains no cure or preventative therapy due to the intransigence nature of this flavivirus. Its tenacity to resist antiviral therapy has left the scientific community with the burden of finding new and accelerated techniques to curb this virus. The onus is on scientists to probe further into understanding the Dengue virus by the use of cheminformatics and bioinformatics tools in the pursuit for an inhibitor against this pernicious virus. Of the Dengue structural and non-structural enzymes, the NS5 RNA-dependent RNA polymerase has been established as a promising target due to its conserved structure amongst all serotypes and its lack of an enzymatic counterpart in mammalian cells. Attempts have been made to design vaccines and small drug molecules as potential inhibitors against DENV. The virus however is resilient, and exists in 5 serotypes with numerous strains under them, thwarting the efforts of researchers to curb its spread. This prompted us to design a study that would address the above challenges by use of CADD tools, which elaborated on the design of target-specific inhibitors of DENV from an atomistic perspective. This included a pharmacophoric approach, which utilized computational software to map out a pharmacophore model against multiple flaviviruses, as well as a focused review on DENV serotype 2 and 3, which included a route map toward the design of target-specific DENV RdRp inhibitors. We believe that these findings will aid in mitigating the effects of the DENV in the lives of compromised individuals, as well as prevent the transmission of DENV from patients to healthy individuals

Chemical Information Bulletin

Periodic supplement for "the regular journals of the American Chemical Society," containing annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the American Chemical Society (ACS) Division of Chemical Literature

Chemical Information Bulletin

Created as a supplement for "the regular journals of the American Chemical Society," this publication contains annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the American Chemical Society (ACS) Division of Chemical Information (CINF)

The Value of New Scientific Communication Models for Chemistry

This paper is intended as a starting point for discussion on the possible future of scientific communication in chemistry, the value of new models of scientific communication enabled by web based technologies, and the necessary future steps to achieve the benefits of those new models. It is informed by a NSF sponsored workshop that was held on October 23-24, 2008 in Washington D.C. It provides an overview on the chemical communication system in chemistry and describes efforts to enhance scientific communication by introducing new web-based models of scientific communication. It observes that such innovations are still embryonic and have not yet found broad adoption and acceptance by the chemical community. The paper proceeds to analyze the reasons for this by identifying specific characteristics of the chemistry domain that relate to its research practices and socio-economic organization. It hypothesizes how these may influence communication practices, and produce resistance to changes of the current system similar to those that have been successfully deployed in other sciences and which have been proposed by pioneers within chemistry.National Science Foundation, Microsof

Text Mining for Chemical Compounds

Exploring the chemical and biological space covered by patent and journal publications is crucial in early- stage medicinal chemistry activities. The analysis provides understanding of compound prior art, novelty checking, validation of biological assays, and identification of new starting points for chemical exploration. Extracting chemical and biological entities from patents and journals through manual extraction by expert curators can take substantial amount of time and resources. Text mining methods can help to ease this process. In this book, we addressed the lack of quality measurements for assessing the correctness of structural representation within and across chemical databases; lack of resources to build text-mining systems; lack of high performance systems to extract chemical compounds from journals and patents; and lack of automated systems to identify relevant compounds in patents. The consistency and ambiguity of chemical identifiers was analyzed within and between small- molecule databases in Chapter 2 and Chapter 3. In Chapter 4 and Chapter 7 we developed resources to enable the construction of chemical text-mining systems. In Chapter 5 and Chapter 6, we used community challenges (BioCreative V and BioCreative VI) and their corresponding resources to identify mentions of chemical compounds in journal abstracts and patents. In Chapter 7 we used our findings in previous chapters to extract chemical named entities from patent full text and to classify the relevancy of chemical compounds

Machine Learning Approaches for Improving Prediction Performance of Structure-Activity Relationship Models

In silico bioactivity prediction studies are designed to complement in vivo and in vitro efforts to assess the activity and properties of small molecules. In silico methods such as Quantitative Structure-Activity/Property Relationship (QSAR) are used to correlate the structure of a molecule to its biological property in drug design and toxicological studies. In this body of work, I started with two in-depth reviews into the application of machine learning based approaches and feature reduction methods to QSAR, and then investigated solutions to three common challenges faced in machine learning based QSAR studies. First, to improve the prediction accuracy of learning from imbalanced data, Synthetic Minority Over-sampling Technique (SMOTE) and Edited Nearest Neighbor (ENN) algorithms combined with bagging as an ensemble strategy was evaluated. The Friedman’s aligned ranks test and the subsequent Bergmann-Hommel post hoc test showed that this method significantly outperformed other conventional methods. SMOTEENN with bagging became less effective when IR exceeded a certain threshold (e.g., \u3e40). The ability to separate the few active compounds from the vast amounts of inactive ones is of great importance in computational toxicology. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p \u3c 0.001, ANOVA) by 22-27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Lastly, current features used for QSAR based machine learning are often very sparse and limited by the logic and mathematical processes used to compute them. Transformer embedding features (TEF) were developed as new continuous vector descriptors/features using the latent space embedding from a multi-head self-attention. The significance of TEF as new descriptors was evaluated by applying them to tasks such as predictive modeling, clustering, and similarity search. An accuracy of 84% on the Ames mutagenicity test indicates that these new features has a correlation to biological activity. Overall, the findings in this study can be applied to improve the performance of machine learning based Quantitative Structure-Activity/Property Relationship (QSAR) efforts for enhanced drug discovery and toxicology assessments

Chemical Information Bulletin

Created as a supplement for "the regular journals of the American Chemical Society," this publication contains annotated bibliographies of chemical documentation literature as well as information about meetings, conferences, awards, scholarships, and other news from the American Chemical Society (ACS) Division of Chemical Information (CINF)