Chemical Abstracts Service Chemical Registry System. 10. Registration of substances from pre-1965 indexes of Chemical Abstracts

Hamill, Karen A.; Nelson, R. David; Stouw, Gerald G. Vander; Stobaugh, Robert E.

doi:10.1021/ci00060a002

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…For medicinal products and associated information, these are the ISO standards for the Identification of Medicinal Products (IDMP), which are mainly implemented in a regulatory context. IDMP-compliant code systems to identify pharmaceutical ingredients include the ASK-numbers from the German Drug Substance Catalogue (Arzneimittel-Stoffkatalog ASK) [6], the Unique Ingredient Identifier (UNII) from the US Substance Registration System [7], CAS Registry Numbers from the Chemical Abstracts Service (CAS) [8] or SNOMED CT codes. For the type of application and dosage form, the EDQM lists of Standard Terms (European Directorate for the Quality of Medicines and Health Care) "Routes and Methods of Administration" and "Pharmaceutical Dose Forms" should be used.…”

Section: Conceptmentioning

confidence: 99%

Fast Healthcare Interoperability Resources (FHIR®) Representation of Medication Data Derived from German Procedure Classification Codes (OPS) Using Identification of Medicinal Products (IDMP) Compliant Terminology

Saß

Zabka

Essenwanger

et al. 2021

German Medical Data Sciences: Bringing Data to Life

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Electronic documentation of medication data is one of the biggest challenges associated with digital clinical documentation. Despite its importance, it has not been consistently implemented in German university hospitals. In this paper we describe the approach of the German Medical Informatics Initiative (MII) towards the modelling of a medication core dataset using FHIR® profiles and standard-compliant terminologies. The FHIR profiles for Medication and MedicationStatement were adapted to the core dataset of the MIl. The terminologies to be used were selected based on the criteria of the ISO-standard for the Identification of Medicinal Products (IDMP). For a first use case with a minimal medication dataset, the entries in the medication chapter of the German Procedure Classification (OPS codes) were analyzed and mapped to IDMP-compliant medication terminology. OPS data are available at all German hospitals as they are mandatory for reimbursement purposes. Reimbursement-relevant encounter data containing OPS medication procedures were used to create a FHIR representation based on the FHIR profiles MedicationStatement and Medication. This minimal solution includes – besides the details on patient and start-/end-dates – the active ingredients identified by the IDMP-compliant codes and – if specified in the OPS code – the route of administration and the range of the amount of substance administered to the patient, using the appropriate unit of measurement code. With FHIR, the medication data can be represented in the data integration centers of the MII to provide a standardized format for data analysis across the MII sites.

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Section: Conceptmentioning

confidence: 99%

Fast Healthcare Interoperability Resources (FHIR®) Representation of Medication Data Derived from German Procedure Classification Codes (OPS) Using Identification of Medicinal Products (IDMP) Compliant Terminology

Saß

Zabka

Essenwanger

et al. 2021

German Medical Data Sciences: Bringing Data to Life

View full text Add to dashboard Cite

show abstract

“…Common ones are the Chemical Abstracts Service (CAS) connection table used in the REGISTRY system [3][4][5][6][7][8][9][10][11][12][13][14][15]; molfile, SDfile and other file standards developed by MDL, now Symyx [16,17]; the International Union of Pure and Applied Chemistry (IU-PAC) International Chemical Identifier, InChI [18]; and SMILES, developed by Daylight Chemical Information Systems [19,20].…”

Section: Chemical Structure Representationmentioning

confidence: 99%

Tautomerism in chemical information management systems

Warr¹

2010

J Comput Aided Mol Des

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Tautomerism has an impact on many of the processes in chemical information management systems including novelty checking during registration into chemical structure databases; storage of structures; exact and substructure searching in chemical structure databases; and depiction of structures retrieved by a search. The approaches taken by 27 different software vendors and database producers are compared. It is hoped that this comparison will act as a discussion document that could ultimately improve databases and software for researchers in the future.

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“…In any case, the number of molecules is too large to explore exhaustively. Currently, more than 68 million molecules are registered in the chemical abstracts service (CAS) of Americal Chemical Society 8 , 9 , and some accessible online molecular databases, e.g., PubChem 10 , ZINC 11 , have been constructed. Moreover, owing to recent advances in high throughput screening, chemoinformatics 12 , and machine learning 13 , many chemical compounds have been discovered from chemical space in the fields of organic light-emitting diode 14 , organic synthesis 15 , and catalyst 16 .…”

Section: Introductionmentioning

confidence: 99%

Selecting molecules with diverse structures and properties by maximizing submodular functions of descriptors learned with graph neural networks

Nakamura

Sakaue

Fujii

et al. 2022

Sci Rep

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Selecting diverse molecules from unexplored areas of chemical space is one of the most important tasks for discovering novel molecules and reactions. This paper proposes a new approach for selecting a subset of diverse molecules from a given molecular list by using two existing techniques studied in machine learning and mathematical optimization: graph neural networks (GNNs) for learning vector representation of molecules and a diverse-selection framework called submodular function maximization. Our method, called SubMo-GNN, first trains a GNN with property prediction tasks, and then the trained GNN transforms molecular graphs into molecular vectors, which capture both properties and structures of molecules. Finally, to obtain a subset of diverse molecules, we define a submodular function, which quantifies the diversity of molecular vectors, and find a subset of molecular vectors with a large submodular function value. This can be done efficiently by using the greedy algorithm, and the diversity of selected molecules measured by the submodular function value is mathematically guaranteed to be at least 63% of that of an optimal selection. We also introduce a new evaluation criterion to measure the diversity of selected molecules based on molecular properties. Computational experiments confirm that our SubMo-GNN successfully selects diverse molecules from the QM9 dataset regarding the property-based criterion, while performing comparably to existing methods regarding standard structure-based criteria. We also demonstrate that SubMo-GNN with a GNN trained on the QM9 dataset can select diverse molecules even from other MoleculeNet datasets whose domains are different from the QM9 dataset. The proposed method enables researchers to obtain diverse sets of molecules for discovering new molecules and novel chemical reactions, and the proposed diversity criterion is useful for discussing the diversity of molecular libraries from a new property-based perspective.

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Chemical Abstracts Service Chemical Registry System. 10. Registration of substances from pre-1965 indexes of Chemical Abstracts

Cited by 12 publications

References 4 publications

Fast Healthcare Interoperability Resources (FHIR®) Representation of Medication Data Derived from German Procedure Classification Codes (OPS) Using Identification of Medicinal Products (IDMP) Compliant Terminology

Fast Healthcare Interoperability Resources (FHIR®) Representation of Medication Data Derived from German Procedure Classification Codes (OPS) Using Identification of Medicinal Products (IDMP) Compliant Terminology

Tautomerism in chemical information management systems

Selecting molecules with diverse structures and properties by maximizing submodular functions of descriptors learned with graph neural networks

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