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ABSTRACT: A data set containing 686 Src kinase inhibitors and 1,941 Src kinase non-binding decoys was collected and used to build two classification models to distinguish inhibitors from decoys. The data set was randomly split into a training set (458 inhibitors and 972 decoys) and a test set (228 inhibitors and 969 decoys). Each molecule was represented by five global molecular descriptors and 18 2D property autocorrelation descriptors calculated using the program ADRIANA.Code. Two machine learning methods, a Kohonen's self-organizing map (SOM) and a support vector machine (SVM), were utilized for the training and classification. For the test set, classification accuracy (ACC) of 99.92% and Matthews correlation coefficient (MCC) of 0.98 were achieved for the SOM model; ACC of 99.33% and MCC of 0.98 were obtained for the SVM model. Some molecular properties, such as molecular weight, number of atoms in a molecule, hydrogen bond properties, polarizabilities, electronegativities, and hydrophobicities, were found to be important for the inhibition of Src kinase.
Molecular Diversity 11/2012; 17(1):75-83. · 3.15 Impact Factor
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ABSTRACT: Using a support vector machine (SVM), two computational models were built to predict whether a compound is an active or weakly active strand transfer (ST) inhibitor based on a dataset of 1257 ST inhibitors of HIV-1 integrase. The model built with MACCS fingerprints gave a prediction accuracy of 91.82% and a Matthews Correlation Coeffiient (MCC) of 0.73 on test set, and the model built with 40 MOE descriptors gave a prediction accuracy of 93.64% and an MCC of 0.79 on test set. Some molecular properties such as electrostatic properties, van der Waals surface area, hydrogen bond properties and the number of fluorine atoms are important factors influencing the interactions between the inhibitor and the integrase. Some scaffolds like β-diketo acid and its derivatives, naphthyridine carboxamide or the isosteric of it and pyrimidionones may play crucial rule to the activity of the HIV-1 integrase inhibitors.
Combinatorial chemistry & high throughput screening 08/2012; 15(10):792-805. · 2.46 Impact Factor
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ABSTRACT: Acetylcholinesterase has long been considered as a target for Alzheimer disease therapy. In this work, several classification models were built for the purpose of distinguishing acetylcholinesterase inhibitors (AChEIs) and decoys. Each molecule was initially represented by 211 ADRIANA.Code and 334 MOE descriptors. Correlation analysis, F-score and attribute selection methods in Weka were used to find the best reduced set of descriptors, respectively. Additionally, models were built using a Support Vector Machine and evaluated by 5-, 10-fold and leave-one-out cross-validation. The best model gave a Matthews Correlation Coefficient (MCC) of 0.99 and a prediction accuracy (Q) of 99.66% for the test set. The best model also gave good result on an external test set of 86 compounds (Q=96.51%, MCC=0.93). The descriptors selected by our models suggest that H-bond and hydrophobicity interactions are important for the classification of AChEIs and decoys.
Combinatorial chemistry & high throughput screening 01/2012; 15(6):492-502. · 2.46 Impact Factor
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ABSTRACT: Toxic myopathy is a muscular disease in which the muscle fibers do not function and which results in muscular weakness. Some drugs, such as lipid-lowering drugs and antihistamines, can cause toxic myopathy. In this work, a dataset containing 232 chemical compounds inducing toxic myopathy (IM-compounds) and 117 drugs not inducing toxic myopathy (notIM-compounds) was collected. The dataset was split into a training set (containing 270 compounds) and a test set (containing 79 compounds). A Kohonen’s self-organizing map (SOM) and a support vector machine (SVM) were applied to develop classification models to differentiate IM-compounds and notIM-compounds. Polarizibity related descriptors, electronegativity related descriptors, atom charges related descriptors, H-bonding related descriptor, atom identity and molecular shape descriptors were used to build models. Using the SOM method, classification accuracies of 88.4 % for the training set and 88.2 % for the test set were achieved; using the SVM method, classification accuracies of 95.6 % for the training set and 86.1 % for the test set were achieved. In addition, extended connectivity fingerprints (ECFP_4) were calculated and analyzed to find important substructures of molecules relating to toxic myopathy.
Molecular Informatics 12/2011; 31(1):27-39. · 2.39 Impact Factor
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ABSTRACT: A multiscale stability study of foams stabilized by sodium dodecyl sulfate (SDS), sodium dodecylbenzene sulfonate (SDBS), and sodium polyoxyethylene alkylether sulfate (AES) was conducted, to investigate the relationship of surfactant molecular behavior and interfacial monolayer configuration of foam film to the foam film properties. Molecular dynamic (MD) simulations using a full-atom model was utilized to explore the microscopic features of the air/liquid interface layer. Several parameters such as the distribution of surfactant head groups and the order degree of surfactant hydrophobic tails were used to describe the molecular adsorption behavior. The effect of molecular structure on the nature of the foam film and the impact on the dynamic stability of wet foam is discussed. In the experimental evaluation, the SDBS foam films manifest strong stiffness and low viscoelasticity as shown by the interfacial shear rheology determination as well as texture analyzer (TA) measurement results, which agree very well with the array behavior of SDBS molecules at the air/water interface as described by the simulation results and is identified to be the reason for the poor dynamic stability. Comparing the molecular structure of SDS, SDBS, and AES, the special contributions of the linking groups such as the O atom, the phenyl group, and the EO (oxyethyl) chain to the interfacial array behavior of surfactants were characterized. It is concluded that microhardness of the foam film enhanced by rigid linking groups favors static foam stability but decreases the dynamic foam stability, while viscoelasticity of the foam film enhanced by soft linking groups increases the dynamic foam stability.
The Journal of Physical Chemistry B 12/2011; 116(1):160-7. · 3.70 Impact Factor
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ABSTRACT: Rhabdomyolysis is a potentially lethal syndrome resulting in leakage of myocyte intracellular contents into the plasma. Some drugs, such as lipid-lowering drugs and antihistamines, can cause rhabdomyolysis. In this work, a dataset containing 186 chemical compounds causing rhabdomyolysis and 117 drugs not causing rhabdomyolysis was collected. The dataset was split into a training set (containing 230 compounds) and a test set (containing 73 compounds). A Kohonen's self-organizing map (SOM) and a support vector machine (SVM) were applied to develop classification models to differentiate compounds causing and not causing rhabdomyolysis. Using the SOM method, classification accuracies of 93.3% for the training set and 84.5% for the test set were achieved; using the SVM method, classification accuracies of 95.2% for the training set and 84.9% for the test set were achieved. In addition, the extended connectivity fingerprints (ECFP_4) for all the molecules were calculated and analyzed to find the important features of molecules relating to rhabdomyolysis.
Toxicology in Vitro 08/2011; 25(8):2017-24. · 2.78 Impact Factor
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ABSTRACT: Several QSAR (quantitative structure-activity relationships) models for predicting the inhibitory activity of 117 Aurora-A kinase inhibitors were developed. The whole dataset was split into a training set and a test set based on two different methods, (1) by a random selection; and (2) on the basis of a Kohonen's self-organizing map (SOM). Then the inhibitory activity of 117 Aurora-A kinase inhibitors was predicted using multilinear regression (MLR) analysis and support vector machine (SVM) methods, respectively. For the two MLR models and the two SVM models, for the test sets, the correlation coefficients of over 0.92 were achieved.
Bioorganic & medicinal chemistry letters 03/2011; 21(8):2238-43. · 2.65 Impact Factor
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ABSTRACT: The adsorption behavior of zwitterionic surfactant dodecyl sulfobetaine (DBS) on a silica/solution interface with Ca(2+), Mg(2+) existing in aqueous solution is explored by atomistic molecular simulations. The interaction energy contribution of van der Waals and electrostatic potentials in the surfactants/water/silica system are respectively calculated, from which the electrical interaction can be found to play a decisive role in the adsorption tendency of DBS on the silica surface with or without inorganic ions, despite different mechanisms. The distinct decrease of energy has been found to be derived from electrical interaction when DBS adsorb on the silica surface covered by Ca(2+) or Mg(2+). Therefore, it can be predicted that the cationic ions combined on the negatively charged silica surface in a mineral water medium might decrease the adsorption trend of DBS on the silica surface, which has been experimentally proven by TOC measurement. Structural information of the close interface layer and the distribution of water molecules are analyzed after the complete molecular dynamics simulation using a ternary model. Ca(2+) and Mg(2+) combined on the silica surface can reduce the adsorption amount of DBS by preventing the direct interaction between DBS and surface, and bringing about the orientation reversal of DBS molecules to break the order of adsorption interface layer. Furthermore, changes in the status of the water spreading on the silica surface caused by the complexation of cations are also an important reason in the adsorption reduction.
The Journal of Physical Chemistry B 07/2010; 114(27):8910-6. · 3.70 Impact Factor
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ABSTRACT: In this work, the perception of similarity of reactions catalyzed by hydrolases and oxidoreductases on the basis of the overall breaking and making of bonds of reactions is investigated. Six physicochemical properties for the reacting bond in the substrate of each enzymatic reaction were calculated to describe the characteristics of each reaction. The 311 reactions catalyzed by hydrolases (EC 3.b.c.d) and the 651 reactions catalyzed by oxidoreductases (EC 1.b.c.d) were classified by Kohonen's self-organizing neural network (KohNN), by a support vector machine (SVM), and by hierarchical clustering analysis (HCA). For the 311 reactions catalyzed by hydrolases, the classification accuracy of 95.8% by a KohNN and 97.7% by an SVM was achieved. For the 651 reactions catalyzed by oxidoreductases, the classification accuracy was 93.4% and 96.3% by a KohNN and a SVM, respectively. The similarities of reactions reflected by the physicochemical effects of reacting bonds were compared with the traditional Enzyme Commission (EC) classification system. The results of a KohNN and a SVM are similar to those of the EC classification system method. However, the perception of similarity of reactions by a KohNN and a SVM shows finer details of the enzymatic reactions and thus could provide a good basis for the comparison of enzymes.
Journal of Chemical Information and Modeling 06/2010; 50(6):1089-100. · 4.68 Impact Factor
