Victor Lobanov | Moscow State University (original) (raw)
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Papers by Victor Lobanov
Journal of Chemical Information and Computer Sciences, 2000
A stochastic procedure for similarity searching in large virtual combinatorial libraries is prese... more A stochastic procedure for similarity searching in large virtual combinatorial libraries is presented. The method avoids explicit enumeration and calculation of descriptors for every virtual compound, yet provides an optimal or nearly optimal similarity selection in a reasonable time frame. It is based on the principle of probability sampling and the recognition that each reagent is represented in a combinatorial library by multiple products. The method proceeds in three stages. First, a small fraction of the products is selected at random and ranked according to their similarity against the query structure. The top-ranking compounds are then identified and deconvoluted into a list of "preferred" reagents. Finally, all the cross-products of these preferred reagents are enumerated in an exhaustive manner, and systematically compared to the target to obtain the final selection. This procedure has been applied to produce similarity selections from several virtual combinatorial libraries, and the dependency of the quality of the selections on several selection parameters has been analyzed.
Langmuir, 1996
domain (head) of the surfactant influence the cmc. The two Relationships between the molecular st... more domain (head) of the surfactant influence the cmc. The two Relationships between the molecular structure and the critical contributions are counteracting, with a lower cmc for a larger micelle concentration (cmc) of anionic surfactants were investihydrophobic domain and a higher cmc for a larger hydrogated using a quantitative structure-property relationship apphilic domain. The current study attempts to define quantitaproach. Measured cmc values for 119 anionic structures, representtive measures for these two counteracting contributions that ing sodium alkyl sulfates and sodium sulfonates with a wide variwill apply over a wide range of anionic surfactant structures.
Journal of Chemical Information and Computer Sciences, Sep 22, 1997
Quantitative structure-property relationships on a large set of descriptors are developed for the... more Quantitative structure-property relationships on a large set of descriptors are developed for the melting points of a large set of mono-and disubstituted benzenes (443 compounds). A correlation equation including nine descriptors (R 2 ) 0.8373) is reported for the whole set of compounds, and six descriptor equations are given for the subsets of ortho-, meta-, and para-substituted compounds, respectively. The importance of the hydrogen bonding descriptor (HDSA 2 ) is demonstrated, and quantum chemical descriptors are successfully applied to obtain predictive models.
Combinatorial Chemistry High Throughput Screening, Apr 1, 2002
One can distinguish between two kinds of virtual combinatorial libraries: "viable" and "accessibl... more One can distinguish between two kinds of virtual combinatorial libraries: "viable" and "accessible". Viable libraries are relatively small in size, are assembled from readily available reagents that have been filtered by the medicinal chemist, and often have a physical counterpart.
The Journal of Physical Chemistry, 1996
A quantitative structure-property relationship (QSPR) treatment of the normal boiling points was ... more A quantitative structure-property relationship (QSPR) treatment of the normal boiling points was performed for a structurally wide variety of organic compounds using the CODESSA (comprehensive descriptors for structural and statistical analysis) technique. A highly significant two-parameter correlation (R 2 ) 0.9544, s ) 16.2 K) employs just two molecular parameters, a bulk cohesiveness descriptor, G I 1/3 , and the area-weighted surface charge of the hydrogen-bonding donor atom(s) in the molecule. A more refined QSPR model (with R 2 ) 0.9732 and s ) 12.4 K) includes, in addition, the most negative atomic partial charge and the number of the chlorine atoms in the molecule. The four-parameter equation offers an average predicted error of 2.3% for a standard set of compounds with an average experimental error of 2.1%. The QSPR equations developed allow remarkably accurate predictions of the normal boiling points for a number of simple inorganic compounds, including water. X Abstract published in AdVance ACS Abstracts, May 15, 1996.
Journal of Chemical Information and Computer Sciences, 2000
A stochastic procedure for similarity searching in large virtual combinatorial libraries is prese... more A stochastic procedure for similarity searching in large virtual combinatorial libraries is presented. The method avoids explicit enumeration and calculation of descriptors for every virtual compound, yet provides an optimal or nearly optimal similarity selection in a reasonable time frame. It is based on the principle of probability sampling and the recognition that each reagent is represented in a combinatorial library by multiple products. The method proceeds in three stages. First, a small fraction of the products is selected at random and ranked according to their similarity against the query structure. The top-ranking compounds are then identified and deconvoluted into a list of "preferred" reagents. Finally, all the cross-products of these preferred reagents are enumerated in an exhaustive manner, and systematically compared to the target to obtain the final selection. This procedure has been applied to produce similarity selections from several virtual combinatorial libraries, and the dependency of the quality of the selections on several selection parameters has been analyzed.
Langmuir, 1996
domain (head) of the surfactant influence the cmc. The two Relationships between the molecular st... more domain (head) of the surfactant influence the cmc. The two Relationships between the molecular structure and the critical contributions are counteracting, with a lower cmc for a larger micelle concentration (cmc) of anionic surfactants were investihydrophobic domain and a higher cmc for a larger hydrogated using a quantitative structure-property relationship apphilic domain. The current study attempts to define quantitaproach. Measured cmc values for 119 anionic structures, representtive measures for these two counteracting contributions that ing sodium alkyl sulfates and sodium sulfonates with a wide variwill apply over a wide range of anionic surfactant structures.
Journal of Chemical Information and Computer Sciences, Sep 22, 1997
Quantitative structure-property relationships on a large set of descriptors are developed for the... more Quantitative structure-property relationships on a large set of descriptors are developed for the melting points of a large set of mono-and disubstituted benzenes (443 compounds). A correlation equation including nine descriptors (R 2 ) 0.8373) is reported for the whole set of compounds, and six descriptor equations are given for the subsets of ortho-, meta-, and para-substituted compounds, respectively. The importance of the hydrogen bonding descriptor (HDSA 2 ) is demonstrated, and quantum chemical descriptors are successfully applied to obtain predictive models.
Combinatorial Chemistry High Throughput Screening, Apr 1, 2002
One can distinguish between two kinds of virtual combinatorial libraries: "viable" and "accessibl... more One can distinguish between two kinds of virtual combinatorial libraries: "viable" and "accessible". Viable libraries are relatively small in size, are assembled from readily available reagents that have been filtered by the medicinal chemist, and often have a physical counterpart.
The Journal of Physical Chemistry, 1996
A quantitative structure-property relationship (QSPR) treatment of the normal boiling points was ... more A quantitative structure-property relationship (QSPR) treatment of the normal boiling points was performed for a structurally wide variety of organic compounds using the CODESSA (comprehensive descriptors for structural and statistical analysis) technique. A highly significant two-parameter correlation (R 2 ) 0.9544, s ) 16.2 K) employs just two molecular parameters, a bulk cohesiveness descriptor, G I 1/3 , and the area-weighted surface charge of the hydrogen-bonding donor atom(s) in the molecule. A more refined QSPR model (with R 2 ) 0.9732 and s ) 12.4 K) includes, in addition, the most negative atomic partial charge and the number of the chlorine atoms in the molecule. The four-parameter equation offers an average predicted error of 2.3% for a standard set of compounds with an average experimental error of 2.1%. The QSPR equations developed allow remarkably accurate predictions of the normal boiling points for a number of simple inorganic compounds, including water. X Abstract published in AdVance ACS Abstracts, May 15, 1996.