Statistical analysis of data file on biologically active compounds II. Prediction of biological activity by substructure analysis method

Avidon, V. V.; Arolovich, V. S.; Kozlova, S. P.; Piruzyan, L. A.

doi:10.1007/bf00777989

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Single-targeted Vs Multi-targeted Virtual Screening1

Introduction1

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2002

2010

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Cited by 3 publications

(2 citation statements)

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“…Different methods can be applied either sequentially or simultaneously. Early attempts to predict many kinds of biological activity simultaneously using such an approach were performed by Avidon and co-authors, 127 Golender and Rozenblit, 64,65 and Vassiliev and co-authors. 128 Since the early 1990s, the authors have been developing the computer program PASS, which predicts many kinds of biological activity based on the structural formula of a compound.…”

Section: Single-targeted Vs Multi-targeted Virtual Screeningmentioning

confidence: 99%

Probabilistic Approaches in Activity Prediction

Filimonov

Poroikov

2008

Chemoinformatics Approaches to Virtual Screening

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Section: Single-targeted Vs Multi-targeted Virtual Screeningmentioning

confidence: 99%

Probabilistic Approaches in Activity Prediction

Filimonov

Poroikov

2008

Chemoinformatics Approaches to Virtual Screening

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“…For analysis of biological activity, a special language, SSFN (Substructure Superposition Fragment Notation), aimed to describe biologically important structural features was devised 23a-d however, recently for this purpose another fragmental approach MNA (“Multilevel Neighborhoods of Atoms”) had been developed. 23e,f …”

Section: Introductionmentioning

confidence: 99%

Fragmental Approach in QSPR

Зефиров

Palyulin

2002

J. Chem. Inf. Comput. Sci.

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Methodological problems of using fragmental descriptors for construction of QSAR/QSPR equations are considered, and the main achievements in this field are summarized and discussed. If a structure-property data set is sufficiently large to allow building statistically significant models, then any topological index can be replaced with a set of fragmental descriptors. Several examples of using the fragmental approach for predicting retention indices and the normal boiling points of organic compounds are considered. Advantages of using fragmental descriptors, namely a "transparency" and interpretability of QSAR/QSPR models, are exemplified.

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Role of physiological factors in predicting the risk of oncological diseases on the basis of the polymorphism of the xenobiotic metabolism enzyme system

Суханов

Piruzyan

2010

Hum Physiol

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Statistical analysis of data file on biologically active compounds II. Prediction of biological activity by substructure analysis method

Cited by 3 publications

References 3 publications

Probabilistic Approaches in Activity Prediction

Probabilistic Approaches in Activity Prediction

Fragmental Approach in QSPR

Role of physiological factors in predicting the risk of oncological diseases on the basis of the polymorphism of the xenobiotic metabolism enzyme system

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