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Alexey Lagunin

Researcher at Russian National Research Medical University

Publications -  135
Citations -  4781

Alexey Lagunin is an academic researcher from Russian National Research Medical University. The author has contributed to research in topics: Medicine & Quantitative structure–activity relationship. The author has an hindex of 31, co-authored 121 publications receiving 3678 citations. Previous affiliations of Alexey Lagunin include Institute of Business & Medical Careers & National Institutes of Health.

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PASS: prediction of activity spectra for biologically active substances

TL;DR: A WWW server for the on-line prediction of the biological activity spectra of substances has been constructed and a WWW interface for the PASS software is developed.
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Prediction of the Biological Activity Spectra of Organic Compounds Using the Pass Online Web Resource

TL;DR: In this paper, the authors present a web resource for the prediction of the biological activity spectra of organic compounds based on their structural formulas for more than 4000 types of biological activity with average accuracy above 95% (http://www.way2drug.com/passonline ).
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QSAR Modelling of Rat Acute Toxicity on the Basis of PASS Prediction

TL;DR: Comparison of accuracy for QSAR models obtained separately using QNA descriptors, PASS predictions, nearest neighbours’ assessment with consensus models clearly demonstrated the benefits of consensus prediction.
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Robustness of biological activity spectra predicting by computer program PASS for noncongeneric sets of chemical compounds.

TL;DR: It is demonstrated that predictions are robust despite the exclusion of up to 60% of information and the influence on the accuracy of predicting the types of activity with PASS by a reduction of the number of structures in the training set and a number of known activities in theTraining set.
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PASS Biological Activity Spectrum Predictions in the Enhanced Open NCI Database Browser

TL;DR: It is shown how the user can conduct complex searches by combining ranges of PASS-predicted probabilities of compounds to be active or to be inactive, respectively, with, e.g., value ranges of physicochemical parameters, presence or absence of particular substructural fragment, and other search criteria.