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The binding affinities to human serum albumin for 94 diverse drugs and drug-like compounds were modeled with the descriptors calculated from the molecular structure alone using a quantitative structure-activity relationship (QSAR) technique. The heuristic method (HM) and support vector machine (SVM) were utilized to construct the linear and nonlinear(More)
The support vector machine (SVM), as a novel type of learning machine, for the first time, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the heat capacity of a diverse set of 182 compounds based on the molecular descriptors calculated from the structure alone. Multiple linear regression (MLR) and radial basis function(More)
The support vector machine (SVM), as a novel type of learning machine, was used to develop a Quantitative Structure-Property Relationship (QSPR) model of the O-H bond dissociation energy (BDE) of 78 substituted phenols. The six descriptors calculated solely from the molecular structures of compounds selected by forward stepwise regression were used as(More)
A least-squares support vector machine (LSSVM) was used for the first time as a novel machine-learning technique for the prediction of the solubility of C60 in a large number of diverse solvents using calculated molecular descriptors from the molecular structure alone and on the basis of the software CODESSA as inputs. The heuristic method of CODESSA was(More)
The wavelet neural network (WNN) was used to predict the programmed-temperature retention values of naphthas. In WNN, a Morlet mother wavelet was used as a transfer function, and the convergence speed was faster than other neural networks. Sixty-four compounds (selected randomly from 94) were used as a training set, and the 30 remaining compounds were used(More)
The support vector machine (SVM), recently developed from machine learning community, was used to develop a nonlinear binary classification model of skin sensitization for a diverse set of 131 organic compounds. Six descriptors were selected by stepwise forward discriminant analysis (LDA) from a diverse set of molecular descriptors calculated from molecular(More)
The logarithmic n-octanol/water partition coefficient (logK(ow)) is a very important property which concerns water-solubility, bioconcentration factor, toxicity and soil absorption coefficient of organic compounds. Quantitative structure-property relationship (QSPR) model for logK(ow) of 133 polychlorinated biphenyls (PCBs) is analyzed using heuristic(More)
Support vector machines (SVMs) were used to develop QSAR models that correlate molecular structures to their toxicity and bioactivities. The performance and predictive ability of SVM are investigated and compared with other methods such as multiple linear regression and radial basis function neural network methods. In the present study, two different data(More)
The Support Vector Machine (SVM) classification algorithm, recently developed from the machine learning community, was used to diagnose breast cancer. At the same time, the SVM was compared to several machine learning techniques currently used in this field. The classification task involves predicting the state of diseases, using data obtained from the UCI(More)
The support vector machine (SVM), which is a novel algorithm from the machine learning community, was used to develop quantitative structure-activity relationship (QSAR) models for predicting the binding affinity of 152 nonapeptides, which can bind to class I MHC HLA-A*201 molecule. Each peptide was represented by a large pool of descriptors including(More)