02540nas a2200301 4500008004100000022001400041245008400055210006900139260011100208300001200319490000700331520149700338653001701835653001801852653002301870653002301893653002401916653001301940653001401953653001401967653002201981100003302003700001502036700002302051700002402074700002602098856011402124 2015 eng d a1386-207300aRole of chemical reactivity and transition state modeling for virtual screening0 aRole of chemical reactivity and transition state modeling for vi aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN a638-6570 v183 a

Every drug discovery research program involves synthesis of a novel and potential drug molecule utilizing atom efficient, economical and environment friendly synthetic strategies. The current work focuses on the role of the reactivity based fingerprints of compounds as filters for virtual screening using a tool ChemScore. A reactant-like (RLS) and a product-like (PLS) score can be predicted for a given compound using the binary fingerprints derived from the numerous known organic reactions which capture the molecule-molecule interactions in the form of addition, substitution, rearrangement, elimination and isomerization reactions. The reaction fingerprints were applied to large databases in biology and chemistry, namely ChEMBL, KEGG, HMDB, DSSTox, and the Drug Bank database. A large network of 1113 synthetic reactions was constructed to visualize and ascertain the reactant product mappings in the chemical reaction space. The cumulative reaction fingerprints were computed for 4000 molecules belonging to 29 therapeutic classes of compounds, and these were found capable of discriminating between the cognition disorder related and anti-allergy compounds with reasonable accuracy of 75% and AUC 0.8. In this study, the transition state based fingerprints were also developed and used effectively for virtual screening in drug related databases. The methodology presented here provides an efficient handle for the rapid scoring of molecular libraries for virtual screening.

10afingerprints10aintermediates10ametabolic pathways10aproduct-like score10areactant-like score10areaction10ascreening10asynthesis10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu1 aTambe, Sanjeev, S.1 aRadhamohan, Deepthi1 aKulkarni, Bhaskar, D. uhttps://library.ncl.res.in/content/role-chemical-reactivity-and-transition-state-modeling-virtual-screening-000617nas a2200145 4500008004100000022001400041245007100055210006900126260011100195300001200306490000700318100003300325700001500358856009800373 2015 eng d a1386-207300aRole of data and methods in chemoinformatics for virtual screening0 aRole of data and methods in chemoinformatics for virtual screeni aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJAN a622-6230 v181 aKarthikeyan, Muthukumarasamy1 aVyas, Renu uhttps://library.ncl.res.in/content/role-data-and-methods-chemoinformatics-virtual-screening-002126nas a2200217 4500008004100000022001400041245008400055210006900139260011100208300001200319490000700331520132700338653002101665653001201686653001601698653001301714653002201727100003301749700001501782856011101797 2015 eng d a1386-207300aRole of open source tools and resources in virtual screening for drug discovery0 aRole of open source tools and resources in virtual screening for aEXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATESbBENTHAM SCIENCE PUBL LTDcJUL a528-5430 v183 a

Advancement in chemoinformatics research in parallel with availability of high performance computing platform has made handling of large scale multi-dimensional scientific data for high throughput drug discovery easier. In this study we have explored publicly available molecular databases with the help of open-source based integrated in-house molecular informatics tools for virtual screening. The virtual screening literature for past decade has been extensively investigated and thoroughly analyzed to reveal interesting patterns with respect to the drug, target, scaffold and disease space. The review also focuses on the integrated chemoinformatics tools that are capable of harvesting chemical data from textual literature information and transform them into truly computable chemical structures, identification of unique fragments and scaffolds from a class of compounds, automatic generation of focused virtual libraries, computation of molecular descriptors for structure-activity relationship studies, application of conventional filters used in lead discovery along with in-house developed exhaustive PTC (Pharmacophore, Toxicophores and Chemophores) filters and machine learning tools for the design of potential disease specific inhibitors. A case study on kinase inhibitors is provided as an example.

10aChemoinformatics10akinases10aopen source10ascaffold10avirtual screening1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu uhttps://library.ncl.res.in/content/role-open-source-tools-and-resources-virtual-screening-drug-discovery-002031nas a2200253 4500008004100000022001400041245010200055210006900157260000800226300001400234490000700248520121200255653001301467653001501480653001801495653001701513653002101530653001601551100002001567700001801587700003301605700001501638856012401653 2022 eng d a1568-026600aReview on computational analysis of big data in breast cancer for predicting potential biomarkers0 aReview on computational analysis of big data in breast cancer fo cSEP a1793-18100 v223 a

Breast cancer is the most predominantly occurring cancer in the world. Several genes and proteins have been recently studied to predict biomarkers that enable early disease identification and monitor its recurrence. In the era of high-throughput technology, studies show several applications of big data for identifying potential biomarkers. The review aims to provide a comprehensive overview of big data analysis in breast cancer towards the prediction of biomarkers with emphasis on computational methods like text mining, network analysis, next-generation sequencing technology (NGS), machine learning (ML), deep learning (DL), and precision medicine. Integrating data from various computational approaches enables the stratification of cancer patients and the identification of molecular signatures in cancer and their subtypes. The computational methods and statistical analysis help expedite cancer prognosis and develop precision cancer medicine (PCM). As a part of case study in the present work, we constructed a large gene-drug interaction network to predict new biomarkers genes. The gene-drug network helped us to identify eight genes that could serve as novel potential biomarkers.

10aBig data10aBiomarkers10aBreast cancer10aDriver genes10aNetwork analysis10atext mining1 aShaikh, Nilofer1 aBapat, Sanket1 aKarthikeyan, Muthukumarasamy1 aVyas, Renu uhttps://library.ncl.res.in/content/review-computational-analysis-big-data-breast-cancer-predicting-potential-biomarkers