Biomedical subjects

V S Lobanov

Publications and source records attributed to V S Lobanov.

3 recordsLinked to original sources

PubMed2001

Combinatorial networks.

A novel approach for the analysis and virtual screening of large combinatorial libraries is presented. The method attempts to relieve the computational burden by computing the properties of the products in a way that does not require their explicit enumeration. In particular, a small subset of compounds from the virtual library is identified and their descriptors are calculated in a conventional manner. The resulting data is used as input to a multilayer perceptron, which is trained to predict the descriptors of the products from the descriptors of their respective building blocks. Once trained, the neural network is able to estimate the descriptors of the remaining members of the virtual library with remarkable accuracy, without ever, generating their connection tables. This method eliminates the two most time-consuming steps in virtual screening and allows the processing of very large combinatorial libraries that are intractable with conventional techniques.

Chemistry Techniques, Analytical↗

PubMed2001

High-density miniaturized thermal shift assays as a general strategy for drug discovery.

More general and universally applicable drug discovery assay technologies are needed in order to keep pace with the recent advances in combinatorial chemistry and genomics-based target generation. Ligand-induced conformational stabilization of proteins is a well-understood phenomenon in which substrates, inhibitors, cofactors, and even other proteins provide enhanced stability to proteins on binding. This phenomenon is based on the energetic coupling of the ligand-binding and protein-melting reactions. In an attempt to harness these biophysical properties for drug discovery, fully automated instrumentation was designed and implemented to perform miniaturized fluorescence-based thermal shift assays in a microplate format for the high throughput screening of compound libraries. Validation of this process and instrumentation was achieved by investigating ligand binding to more than 100 protein targets. The general applicability of the thermal shift screening strategy was found to be an important advantage because it circumvents the need to design and retool new assays with each new therapeutic target. Moreover, the miniaturized thermal shift assay methodology does not require any prior knowledge of a therapeutic target's function, making it ideally suited for the quantitative high throughput drug screening and evaluation of targets derived from genomics.

Estrogen Receptor alpha↗

PubMedDate not supplied

Nonlinear mapping networks.

Among the many dimensionality reduction techniques that have appeared in the statistical literature, multidimensional scaling and nonlinear mapping are unique for their conceptual simplicity and ability to reproduce the topology and structure of the data space in a faithful and unbiased manner. However, a major shortcoming of these methods is their quadratic dependence on the number of objects scaled, which imposes severe limitations on the size of data sets that can be effectively manipulated. Here we describe a novel approach that combines conventional nonlinear mapping techniques with feed-forward neural networks, and allows the processing of data sets orders of magnitude larger than those accessible with conventional methodologies. Rooted on the principle of probability sampling, the method employs a classical algorithm to project a small random sample, and then "learns" the underlying nonlinear transform using a multilayer neural network trained with the back-propagation algorithm. Once trained, the neural network can be used in a feed-forward manner to project the remaining members of the population as well as new, unseen samples with minimal distortion. Using examples from the fields of image processing and combinatorial chemistry, we demonstrate that this method can generate projections that are virtually indistinguishable from those derived by conventional approaches. The ability to encode the nonlinear transform in the form of a neural network makes nonlinear mapping applicable to a wide variety of data mining applications involving very large data sets that are otherwise computationally intractable.

Journal Article↗