struct=name program (CambridgeSoft Corporation)
90
Structured Review
CambridgeSoft Corporation
struct=name program
Struct=Name Program, supplied by CambridgeSoft Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/struct%3Dname/struct+name/pm24236924-298-1-1
Average 90 stars, based on 1 article reviews
Struct=Name Program, supplied by CambridgeSoft Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/struct%3Dname/struct+name/pm24236924-298-1-1
Average 90 stars, based on 1 article reviews
struct=name program - by Bioz Stars,
2026-10
90/100 stars
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other:Article Title: Improved chemical text mining of patents with infinite dictionaries and automatic spelling correction. Article Snippet: The text mining of patents of pharmaceutical interest poses a number of unique challenges not encountered in other fields of text mining.. Unlike fields, such as bioinformatics, where the number of terms of interest is enumerable and essentially static, systematic chemical nomenclature can describe an infinite number of molecules.. Hence, the dictionaryand ontology-based techniques that are commonly used for gene names, diseases, species, etc., have limited utility when searching for novel therapeutic compounds in patents. Article Title: Pyrrolo[2,3-B]pyrazines as SYK inhibitors Article Snippet: In general, the nomenclature used in this application is based on AUTONOMTM v.4.0, a Beilstein Institute computerized system or Article Title: Chemical Name Extraction Based on Automatic Training Data Generation and Rich Feature Set Article Snippet: The automation of extracting chemical names from text has significant value to biomedical and life science research.. A major barrier in this task is the difficulty of getting a sizable and good quality data to train a reliable entity extraction model. Another difficulty is the selection of informative features of chemical names, since comprehensive domain knowledge on chemistry nomenclature is required.. Leveraging random text generation techniques, we explore the idea of automatically creating training sets for the task of chemical name extraction. Article Title: 1 H-pyrazole and 4,5-disubstituted thiazole inhibitors of SYK Article Snippet: In general, the nomenclature used in this Application is based on AUTONOMTM v.4.0, a Beilstein Institute computerized system or Article Title: Compound Article Snippet: The title and sub-title compounds of the examples and preparations were named using the Article Title: Chemical name to structure: OPSIN, an open source solution. Article Snippet: With the ever increasing size of the scientific literature in the form of journal articles, patents, and theses, it is becoming infeasible for a scientist to manually view all the relevant literature.. This has led to the creation of the field of chemical text mining to facilitate structure searching of the literature and to harness the power of computers to find novel relationships.. All such techniques rely on chemistry being presented in a computer-readable form. Article Title: Building structure-activity insights through patent mining. Article Snippet: 1Pfizer Worldwide Research & Development, 620 Memorial Drive, Cambridge, MA 02139, USA 2Pfizer Worldwide Research & Development, Eastern Point Road, Groton, CT 06340, USA *Author for correspondence: E-mail: meihua.tu@pfizer.com One gap in current patent-mining practice is the lack of tools to build SAR knowledge.. Here, we report a novel technique that enabled us to derive useful SAR information from the exemplified structures of a series of patents.. In our approach, exemplified chemical structures were extracted from patent documents. |