Biomedical subjects

Masamoto Arakawa

Publications and source records attributed to Masamoto Arakawa.

5 recordsLinked to original sources

PubMed2003

Simultaneous determination of bioactive conformations and alignment rules by multi-way PLS modeling.

In this study, we propose a new three-dimensional quantitative structure-activity relationship (3D-QSAR) method for selecting bioactive conformations and alignment rule simultaneously. The possible conformations of all molecules are generated by conformational analysis and they are superimposed on template conformer with possible alignment rules. The field variables are calculated as 3D descriptor of structures. Four-way partial least-squares (PLS) analysis is applied, and the conformations and alignment rule largely contributing to biological activity are selected. In order to demonstrate this method, the data set of benzodiazepine derivatives, antagonists of (CCK-B), was used as a test sample. As a result, appropriate conformers and alignment rule were selected and significant PLS model was obtained. The resulting final model could give the reasonable 3D coefficient contour maps. Moreover, external prediction was carried out by use of external data sample and its prediction was proved to be high enough.

Algorithms↗

PubMed2003

Multi-way PLS modeling of structure-activity data by incorporating electrostatic and lipophilic potentials on molecular surface.

We devised and elaborated a surface-based three-dimensional-quantitative structure-activity relationship (3D-QSAR) method, which had been proposed in the previous study. This approach can be applied to more general case where both the electrostatic and lipophilic potentials on molecular surface simultaneously change. The 3D coordinates of all sampling points on molecular surface are projected into a 2D map by Kohonen neural network (KNN). Each node in the map is coded by the associated molecular electrostatic potential (MEP) or molecular lipophilic potential (MLP) values. The electrostatic and lipophilic KNN maps are generated for each compound and the four-way array is constructed by collecting two KNN maps of all samples. The correlation between four-way array and biological activity is examined by four-way partial least-squares (PLS). For validation, the structure-activity data of estrogen receptor antagonists was investigated. The four-way PLS model gave the high statistics at calibration and validation stages. The coefficients of the four-way PLS model back-projected on molecular surface had a reasonable 3D distribution and it was nicely consistent with active site of the estrogen receptor which was recently made clear by X-ray crystallography.

Journal Article↗

PubMed2002

New molecular surface-based 3D-QSAR method using Kohonen neural network and 3-way PLS.

Comparative molecular field analysis (CoMFA) has been widely used as a standard three dimensional quantitative structure-activity relationship (3D-QSAR) method. Although CoMFA is a useful technique, it does not always reflect real ligand-receptor interaction. Molecular interactions between the ligand and receptor are mainly occurred near the van der Waals surface of ligand. All grid points surrounding whole molecule in CoMFA are not important as molecular descriptors. If each molecule is represented by physico-chemical parameters on molecular surface, more precise and realistic 3D-QSAR is possible. We developed a new surface-based 3D-QSAR method using Kohonen neural network (KNN) and three-way partial least squares (3-way PLS). This method was applied to 25 dopamine 2 (D2) receptor antagonists for validation. First, the 3D coordinates of all sampling points on the van der Waals surface were projected into the 2D map by KNN. Each node in the map was coded by the associated molecular electrostatic potential (MEP) value of the original sampling point. Then, the correlation between the MEP values of all 2D maps and D2 receptor antagonist activities was analyzed by 3-way PLS. The statistics of the 3-way PLS model was excellent and the coefficients back-projected on the van der Waals surface had reasonable 3D distribution. Lastly, all data was divided into the calibration and validation sets by D-optimal designs and the activities of validation set were predicted. The external validation suggested that 3-way PLS is better than standard (2-way) PLS for prediction.

Dopamine Antagonists↗

PubMedDate not supplied

Novel alignment method of small molecules using the Hopfield Neural Network.

Molecular alignment is an important step in three-dimensional quantitative structure-activity relationship (3D-QSAR) such as comparative molecular field analysis (CoMFA). A reasonable molecular alignment is necessary for building a 3D-QSAR model. In this paper, a novel method for molecular alignment using the Hopfield Neural Network (HNN) is introduced. Four kinds of chemical properties are assigned to each atom of a molecule. Then, those properties between two molecules correspond to each other using HNN. To validate our method, HNN was applied to 12 pairs of enzyme inhibitors cited from the Protein Data Bank (PDB). As a result, our method could successfully reproduce the real molecular alignments obtained from X-ray crystallography.

Crystallography, X-Ray↗

PubMedDate not supplied

Application of the novel molecular alignment method using the Hopfield Neural Network to 3D-QSAR.

Recently, we investigated and proposed the novel molecular alignment method with the Hopfield Neural Network (HNN). Molecules are represented by four kinds of chemical properties (hydrophobic group, hydrogen-bonding acceptor, hydrogen-bonding donor, and hydrogen-bonding donor/acceptor), and then those properties between two molecules correspond to each other using HNN. The 12 pairs of enzyme-inhibitors were used for validation, and our method could successfully reproduce the real molecular alignments obtained from X-ray crystallography. In this paper, we apply the molecular alignment method to three-dimensional quantitative structure-activity relationship (3D-QSAR) analysis. The two data sets (human epidermal growth factor receptor-2 inhibitors and cyclooxygenase-2 inhibitors) were investigated to validate our method. As a result, the robust and predictive 3D-QSAR models were successfully obtained in both data sets.

Cyclooxygenase 2↗