Literature DB >> 31120751

Comparing Molecular Patterns Using the Example of SMARTS: Theory and Algorithms.

Robert Schmidt1, Emanuel S R Ehmki1, Farina Ohm1, Hans-Christian Ehrlich1, Andriy Mashychev1, Matthias Rarey1.   

Abstract

Molecular patterns are widely used for compound filtering in molecular design endeavors. They describe structural properties that are connected with unwanted physical or chemical properties like reactivity or toxicity. With filter sets comprising hundreds of structural filters, an analytic approach to compare those patterns is needed. Here we present a novel approach to solve the generic pattern comparison problem. We introduce chemically inspired fingerprints for pattern nodes and edges to derive an easy-to-compare pattern representation. On two annotated pattern graphs we apply a maximum common subgraph algorithm enabling the calculation of pattern inclusion and similarity. The resulting algorithm can be used in many different ways. We can automatically derive pattern hierarchies or search in large pattern collections for more general or more specific patterns. To the best of our knowledge, the presented algorithm is the first of its kind enabling these types of chemical pattern analytics. Our new tool named SMARTScompare is an implementation of the approach for the SMARTS language, which is the quasi-standard for structural filters. We demonstrate the capabilities of SMARTScompare on a large collection of SMARTS patterns from real applications.

Entities:  

Year:  2019        PMID: 31120751     DOI: 10.1021/acs.jcim.9b00250

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  4 in total

1.  Accurate description of molecular dipole surface with charge flux implemented for molecular mechanics.

Authors:  Xudong Yang; Chengwen Liu; Brandon D Walker; Pengyu Ren
Journal:  J Chem Phys       Date:  2020-08-14       Impact factor: 3.488

2.  Influence of Template Size, Canonicalization, and Exclusivity for Retrosynthesis and Reaction Prediction Applications.

Authors:  Esther Heid; Jiannan Liu; Andrea Aude; William H Green
Journal:  J Chem Inf Model       Date:  2021-12-23       Impact factor: 4.956

3.  Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.

Authors:  John A Keith; Valentin Vassilev-Galindo; Bingqing Cheng; Stefan Chmiela; Michael Gastegger; Klaus-Robert Müller; Alexandre Tkatchenko
Journal:  Chem Rev       Date:  2021-07-07       Impact factor: 60.622

4.  SMARTS.plus - A Toolbox for Chemical Pattern Design.

Authors:  Christiane Ehrt; Bennet Krause; Robert Schmidt; Emanuel S R Ehmki; Matthias Rarey
Journal:  Mol Inform       Date:  2020-10-08       Impact factor: 3.353

  4 in total

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