Mapping Chemical Structures to Markush Structures Using SMIRKS

Deng, Wei; Schneider, Gisbert; So, W. Venus

doi:10.1002/minf.201100041

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…Many of the reaction rules used the reaction SMARTS syntax provided by the open-source cheminformatics library RDKit (Table ). Reaction SMARTS syntax is derived from SMARTS patterns, and has similarities to the alternative reaction languages “SMIRKS” , and “SMILES” . Reaction SMARTS are a compact method for encoding transformations that occur on a small number of localized atoms.…”

Section: Methodsmentioning

confidence: 99%

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites

Hughes

Dang

Kumar

et al. 2020

J. Chem. Inf. Model.

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Adverse drug metabolism often severely impacts patient morbidity and mortality. Unfortunately, drug metabolism experimental assays are costly, inefficient, and slow. Instead, computational modeling could rapidly flag potentially toxic molecules across thousands of candidates in the early stages of drug development. Most metabolism models focus on predicting sites of metabolism (SOMs): the specific substrate atoms targeted by metabolic enzymes. However, SOMs are merely a proxy for metabolic structures: knowledge of an SOM does not explicitly provide the actual metabolite structure. Without an explicit metabolite structure, computational systems cannot evaluate the new molecule's properties. For example, the metabolite's reactivity cannot be automatically predicted, a crucial limitation because reactive drug metabolites are a key driver of adverse drug reactions (ADRs). Additionally, further metabolic events cannot be forecast, even though the metabolic path of the majority of substrates includes two or more sequential steps. To overcome the myopia of the SOM paradigm, this study constructs a well-defined systemtermed the metabolic forestfor generating exact metabolite structures. We validate the metabolic forest with the substrate and product structures from a large, chemically diverse, literature-derived dataset of 20 736 records. The metabolic forest finds a pathway linking each substrate and product for 79.42% of these records. By performing a breadth-first search of depth two or three, we improve performance to 88.43 and 88.77%, respectively. The metabolic forest includes a specialized algorithm for producing accurate quinone structures, the most common type of reactive metabolite. To our knowledge, this quinone structure algorithm is the first of its kind, as the diverse mechanisms of quinone formation are difficult to systematically reproduce. We validate the metabolic forest on a previously published dataset of 576 quinone reactions, predicting their structures with a depth three performance of 91.84%. The metabolic forest accurately enumerates metabolite structures, enabling promising new directions such as joint metabolism and reactivity modeling.

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Section: Methodsmentioning

confidence: 99%

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites

Hughes

Dang

Kumar

et al. 2020

J. Chem. Inf. Model.

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“…Its web based application, iMarVis, revised the underlying R group numbering system to deal with nested R group presentation [8]. An algorithm based on SMIRK language is introduced to solve the query for a compound within a Markush structure [9].…”

Section: Introductionmentioning

confidence: 99%

IntelliPatent: a web-based intelligent system for fast chemical patent claim drafting

Wang

Tseng

2019

J Cheminform

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The first step of automating composition patent drafting is to draft the claims around a Markush structure with substituents. Currently, this process depends heavily on experienced attorneys or patent agents, and few tools are available. IntelliPatent was created to accelerate this process. Users can simply upload a series of analogs of interest, and IntelliPatent will automatically extract the general structural scaffold and generate the patent claim text. The program can also extend the patent claim by adding commonly seen R groups from historical lists of the top 30 selling drugs in the US for all R substituents. The program takes MDL SD file formats as inputs, and the invariable core structure and variable substructures will be identified as the initial scaffold and R groups in the output Markush structure. The results can be downloaded in MS Word format (.docx). The suggested claims can be quickly generated with IntelliPatent. This web-based tool is freely accessible at https://intellipatent.cmdm.tw/.

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Markush Structures and Chemical Patents

Cosgrove

2013

Methods and Principles in Medicinal Chemistry

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Mapping Chemical Structures to Markush Structures Using SMIRKS

Cited by 5 publications

References 8 publications

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites

IntelliPatent: a web-based intelligent system for fast chemical patent claim drafting

Markush Structures and Chemical Patents

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