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• W2023823374 endingPage "1489" @default.
• W2023823374 startingPage "1480" @default.
• W2023823374 abstract "Patent specifications are one of many information sources needed to progress drug discovery projects. Understanding compound prior art and novelty checking, validation of biological assays, and identification of new starting points for chemical explorations are a few areas where patent analysis is an important component. Cheminformatics methods can be used to facilitate the identification of so-called key compounds in patent specifications. Such methods, relying on structural information extracted from documents by expert curation or text mining, can complement or in some cases replace the traditional manual approach of searching for clues in the text. This paper describes and compares three different methods for the automatic prediction of key compounds in patent specifications using structural information alone. For this data set, the cluster seed analysis described by Hattori et al. (Hattori, K.; Wakabayashi, H.; Tamaki, K. Predicting key example compounds in competitors' patent applications using structural information alone. J. Chem. Inf. Model.2008, 48, 135–142) is superior in terms of prediction accuracy with 26 out of 48 drugs (54%) correctly predicted from their corresponding patents. Nevertheless, the two new methods, based on frequency of R-groups (FOG) and maximum common substructure (MCS) similarity measures, show significant advantages due to their inherent ability to visualize relevant structural features. The results of the FOG method can be enhanced by manual selection of the scaffolds used in the analysis. Finally, a successful example of applying FOG analysis for designing potent ATP-competitive AXL kinase inhibitors with improved properties is described." @default.
• W2023823374 created "2016-06-24" @default.
• W2023823374 creator A5043576962 @default.
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• W2023823374 date "2012-06-11" @default.
• W2023823374 modified "2023-09-27" @default.
• W2023823374 title "Exploiting Structural Information in Patent Specifications for Key Compound Prediction" @default.
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• W2023823374 doi "https://doi.org/10.1021/ci3001293" @default.
• W2023823374 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/22639789" @default.
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