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VS Lobanov

Publications and source records attributed to VS Lobanov.

4 recordsLinked to original sources
PubMed2000

Ultrafast algorithm for designing focused combinational arrays

A novel greedy algorithm for the design of focused combinatorial arrays is presented. The method is applicable when the objective function is decomposable to individual molecular contributions and makes use of a heuristic that allows the independent evaluation and ranking of candidate reagents in each variation site in the combinatorial library. The algorithm is extremely fast and convergent and produces solutions that are comparable to and often better than those derived from the substantially more elaborate and computationally intensive stochastic sampling techniques. Typical examples of design objectives that are amendable to this approach include maximum similarity to a known lead (or set of leads), maximum predicted activity according to some structure-activity or receptor binding model, containment within certain molecular property bounds, and many others.

Journal Article↗
PubMed2000

Stochastic similarity selections from large combinatorial libraries

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.

Journal Article↗
PubMed1997

Prediction of Critical Micelle Concentration Using a Quantitative Structure-Property Relationship Approach

Relationships between the molecular structure and the critical micelle concentration (cmc) of anionic surfactants were investigated using a quantitative structure-property relationship approach. Measured cmc values for 119 anionic structures, representing sodium alkyl sulfates and sodium sulfonates with a wide variety of hydrophobic and hydrophilic structures, were considered. The best multiple linear regression model involved three terms (descriptors) and had a correlation coefficient of R2 = 0.940. Very good correlations (R2 = 0.988) were obtained using three descriptors for a subset of 68 structures, with structural variation only in the hydrophobic domain. From the descriptors used in these regressions, one can conclude that the cmc is primarily dependent on the size (volume or surface area) of the hydrophobic domain and to a lesser extent on the structural complexity of the surfactant molecule.

Journal Article↗