Several popular machine learning methodsAssociative Neural Networks (ANN), Support Vector Machines (SVM), k Nearest Neighbors (kNN), modified version of the partial least-squares analysis (PLSM), backpropagation neural network (BPNN), and Multiple Linear Regression Analysis (MLR)implemented in ISIDA, NASAWIN, and VCCLAB software have been used to perform QSPR modeling of melting point of structurally diverse data set of 717 bromides of nitrogen-containing organic cations (FULL) including 126 pyridinium bromides (PYR), 384 imidazolium and benzoimidazolium bromides (IMZ), and 207 quaternary ammonium bromides (QUAT). Several types of descriptors were tested: E-state indices, counts of atoms determined for E-state atom types, molecular descriptors generated by the DRAGON program, and different types of substructural molecular fragments. Predictive ability of the models was analyzed using a 5-fold external cross-validation procedure in which every compound in the parent set was included in one of five test sets. Among the 16 types of developed structure − melting point models, nonlinear SVM, ASNN, and BPNN techniques demonstrate slightly better performance over other methods. For the full set, the accuracy of predictions does not significantly change as a function of the type of descriptors. For other sets, the performance of descriptors varies as a function of method and data set used. The root-mean squared error (RMSE) of prediction calculated on independent test sets is in the range of 37.5−46.4 °C (FULL), 26.2−34.8 °C (PYR), 38.8−45.9 °C (IMZ), and 34.2−49.3 °C (QUAT). The moderate accuracy of predictions can be related to the quality of the experimental data used for obtaining the models as well as to difficulties to take into account the structural features of ionic liquids in the solid state (polymorphic effects, eutectics, glass formation).
J. Chem. Inf. Model., 2007, 47 (3), pp 1111–1122 DOI: 10.1021/ci600493x Publication Date (Web): March 24, 2007

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Докл. АН, сер. Химия, 2007, Т. 417, № 5, C. 639-641

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Докл. РАН, 2009, Т. 427, № 3, C. 335-339

Рассмотрены работы, в которых нейросетевой подход применяется для моделирования зависимостей свойств органических соединений от их структуры. Наряду с основными принципами нейросетевого моделирования обсуждаются также особенности нейросетевого подхода, характерные для представления и классификации структурных химических данных. Приведена краткая информация о нейросетевых моделях спектральных характеристик, реакционной способности, физико-химических свойств и биологической активности органических соединений.
Успехи химии, 2003, T.72, № 7, C. 706-727; RUSS CHEM REV, 2003, 72 (7), 629–649. DOI 10.1070/RC2003v072n07ABEH000754

Two inductive knowledge transfer approaches - multitask learning (MTL) and Feature Net (FN) - have been used to build predictive neural networks (ASNN) and PLS models for 11 types of tissue-air partition coefficients (TAPC). Unlike conventional single-task learning (STL) modeling focused only on a single target property without any relations to other properties, in the framework of inductive transfer approach, the individual models are viewed as nodes in the network of interrelated models built in parallel (MTL) or sequentially (FN). It has been demonstrated that MTL and FN techniques are extremely useful in structure-property modeling on small and structurally diverse data sets, when conventional STL modeling is unable to produce any predictive model. The predictive STL individual models were obtained for 4 out of 11 TAPC whereas application of inductive knowledge transfer techniques resulted in models for 9 TAPC. Differences in prediction performances of the models as a function of the machine-learning method, and of the number of properties simultaneously involved in the learning, has been discussed.
J. Chem. Inf. Model., 2009, V. 49, No. 1, P. 133-144. DOI: 10.1021/ci8002914

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Докл. АН, сер химия, 2010, Т.430, N 5, c. 635-638; Doklady Chemistry, Volume 430, Issue 2, February 2010, Pages 39-42; DOI: 10.1134/S0012500810020023

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Российский химический журнал (Журнал Российского химического общества им. Д.И.Менделеева), 2006, Т. 50, C. 86-96

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In: Chemoinformatics Approaches to Virtual Screening / Varnek A., Tropsha A., Ed. – RCS Publishing. - 2008. – P. 1-43

Here, chemoinformatics is considered as a theoretical chemistry discipline complementary to quantum chemistry and force-field molecular modeling. These three fields are compared with respect to molecular representation, inference mechanisms, basic concepts and application areas. A chemical space, a fundamental concept of chemoinformatics, is considered with respect to complex relations between chemical objects (graphs or descriptor vectors). Statistical Learning Theory, one of the main mathematical approaches in structure-property modeling, is briefly reviewed. Links between chemoinformatics and its “sister” fields – machine learning, chemometrics and bioinformatics are discussed.
Molecular Informatics, 2011, volume 30, issue 1, pages 20-32