01486nas a2200133   4500008004100000245006800041210006800109260001300177300001200190520100600202100003301208700001501241856009601256       2014                        eng d00aMachine learning methods in chemoinformatics for drug discovery0 aMachine learning methods in chemoinformatics for drug discovery  bSpringer  a133-1943 aIt is well known that the structure of a molecule is responsible for its biological activity or physicochemical property. Here, we describe the role of machine learning (ML)/statistical methods for building reliable, predictive models in chemoinformatics. The ML methods are broadly divided into clustering, classification and regression techniques. However, the statistical/mathematical techniques which are part of the ML tools, such as artificial neural networks, hidden Markov models, support vector machine, decision tree learning, Random Forest and Naive Bayes and belief networks, are best suited for drug discovery and play an important role in lead identification and lead optimization steps. This chapter provides stepwise procedures for building ML-based classification and regression models using state-of-art open-source and proprietary tools. A few case studies using benchmark data sets have been carried out to demonstrate the efficacy of the ML-based classification for drug designing1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu  uhttps://library.ncl.res.in/content/machine-learning-methods-chemoinformatics-drug-discovery02426nas a2200241   4500008004100000022001400041245012700055210006900182260011100251300001200362490000700374520149700381653002101878653002001899653001201919653001601931653002201947100003301969700001902002700001902021700001502040856012902055       2015                        eng d  a1386-207300aMegaMiner: a tool for lead identification through text mining using chemoinformatics tools and cloud computing environment0 aMegaMiner a tool for lead identification through text mining usi  aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN  a591-6030 v183 a<p>Virtual screening is an indispensable tool to cope with the massive amount of data being tossed by the high throughput omics technologies. With the objective of enhancing the automation capability of virtual screening process a robust portal termed MegaMiner has been built using the cloud computing platform wherein the user submits a text query and directly accesses the proposed lead molecules along with their drug-like, lead-like and docking scores. Textual chemical structural data representation is fraught with ambiguity in the absence of a global identifier. We have used a combination of statistical models, chemical dictionary and regular expression for building a disease specific dictionary. To demonstrate the effectiveness of this approach, a case study on malaria has been carried out in the present work. MegaMiner offered superior results compared to other text mining search engines, as established by F score analysis. A single query term `malaria' in the portlet led to retrieval of related PubMed records, protein classes, drug classes and 8000 scaffolds which were internally processed and filtered to suggest new molecules as potential anti-malarials. The results obtained were validated by docking the virtual molecules into relevant protein targets. It is hoped that MegaMiner will serve as an indispensable tool for not only identifying hidden relationships between various biological and chemical entities but also for building better corpus and ontologies.</p>10aChemoinformatics10acloud computing10amalaria10atext mining10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aPandit, Yogesh1 aPandit, Deepak1 aVyas, Renu  uhttps://library.ncl.res.in/content/megaminer-tool-lead-identification-through-text-mining-using-chemoinformatics-tools-and-0