The signature molecular descriptor

Churchwell, Carla J; Rintoul, Mark Daniel; Martin, Shawn; Visco, Donald P.; Kotu, Archana; Larson, Richard S.; Sillerud, Laurel O.; Brown, David C.; Faulon, Jean-Loup

doi:10.1016/j.jmgm.2003.10.002

Cited by 95 publications

(39 citation statements)

References 31 publications

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“…The main challenge of in silico molecular design approaches is generation of structural fragments from de facto modeling descriptors. Inverse-QSAR is an attempt in this direction [83][84][85][86][87][88][89]. Developments in new metrics are required to address different features of drug-receptor interactions so far not tamed.…”

Section: Resultsmentioning

confidence: 99%

“…Different algorithms have comeup in inverse QSAR to tackle the reconstruction of molecules from their identified characteristics [85][86][87][88]. The utility of these inverse QSAR approaches have been validated by reconstruction of structures of interest in various studies involving biological [84,88], physicochemical [89] and spectral activities and properties [87].…”

Section: Graph Reconstruction (Inverse Qsar)mentioning

confidence: 99%

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Chemical Structure Indices in In Silico Molecular Design

Prabhakar

2008

Sci Pharm

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Section: Resultsmentioning

confidence: 99%

Section: Graph Reconstruction (Inverse Qsar)mentioning

confidence: 99%

Chemical Structure Indices in In Silico Molecular Design

Prabhakar

2008

Sci Pharm

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“…In order to investigate the security of chemical information exchange, we employ several tools developed previously for the purpose of designing molecules matching specified chemical properties [14][15][16]. These tools are discussed in Section Reverse engineering methods and include a stochastic structure generator as well as a method for deterministic enumeration of molecules.…”

Section: Methodsmentioning

confidence: 99%

“…We have investigated two types of enumeration in previous studies. In [14] we derive a linear system of Diophantine equations whose solutions correspond to all possible molecular signatures that correspond to molecules. In [16] we describe an algorithm, which enumerates all possible molecules that can generate a given molecular signature.…”

Section: Deterministic Enumerationmentioning

confidence: 99%

Reverse engineering chemical structures from molecular descriptors: how many solutions?

Faulon

Brown

Martin

2005

J Comput Aided Mol Des

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Physical, chemical and biological properties are the ultimate information of interest for chemical compounds. Molecular descriptors that map structural information to activities and properties are obvious candidates for information sharing. In this paper, we consider the feasibility of using molecular descriptors to safely exchange chemical information in such a way that the original chemical structures cannot be reverse engineered. To investigate the safety of sharing such descriptors, we compute the degeneracy (the number of structure matching a descriptor value) of several 2D descriptors, and use various methods to search for and reverse engineer structures. We examine degeneracy in the entire chemical space taking descriptors values from the alkane isomer series and the PubChem database. We further use a stochastic search to retrieve structures matching specific topological index values. Finally, we investigate the safety of exchanging of fragmental descriptors using deterministic enumeration.

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“…The result of such inverse analysis was accompanied by many other equations, which assure the existence of chemical structures corresponding to the MLR equation. [16] The problem of retrieving chemical structures corresponding to a specific set of coordinates is called the pre-image problem. [17] The group of Akutsu proved that it is strongly NP-hard even using planar graphs with bounded degrees, supposing descriptors are a feature vector defined as the frequencies of labeled paths.…”

Section: Introductionmentioning

confidence: 99%

Finding Chemical Structures Corresponding to a Set of Coordinates in Chemical Descriptor Space

Miyao

Funatsu

2017

Mol. Inf.

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When chemical structures are searched based on descriptor values, or descriptors are interpreted based on values, it is important that corresponding chemical structures actually exist. In order to consider the existence of chemical structures located in a specific region in the chemical space, we propose to search them inside training data domains (TDDs), which are dense areas of a training dataset in the chemical space. We investigated TDDs' features using diverse and local datasets, assuming that GDB11 is the chemical universe. These two analyses showed that considering TDDs gives higher chance of finding chemical structures than a random search-based method, and that novel chemical structures actually exist inside TDDs. In addition to those findings, we tested the hypothesis that chemical structures were distributed on the limited areas of chemical space. This hypothesis was confirmed by the fact that distances among chemical structures in several descriptor spaces were much shorter than those among randomly generated coordinates in the training data range

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The signature molecular descriptor

Cited by 95 publications

References 31 publications

Chemical Structure Indices in In Silico Molecular Design

Chemical Structure Indices in In Silico Molecular Design

Reverse engineering chemical structures from molecular descriptors: how many solutions?

Finding Chemical Structures Corresponding to a Set of Coordinates in Chemical Descriptor Space

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