Nonlinear Partial Least Squares Modeling of Phenyl Alkylamines with the Monoamine Oxidase Inhibitory Activities

Hasegawa, Kiyoshi; Kimura, Toshiro; Miyashita, Yoshikatsu; Funatsu, Kimito

doi:10.1021/ci960362j

Cited by 21 publications

(8 citation statements)

References 17 publications

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“…Hasegawa et al. conducted QSAR studies using a slightly modified QPLS algorithm 37. The algorithm was revised further by Baffi et al 38…”

Section: Methodsmentioning

confidence: 99%

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Evolution of PLS for Modeling SAR and omics Data

Hasegawa¹,

Funatsu

2012

Molecular Informatics

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In quantitative structure-activity relationship (QSAR), multivariate statistical methods are commonly used for data analysis. Partial least squares (PLS) is of particular interest because it can analyze data with strongly collinear, noisy and numerous X variables, and also simultaneously models several activity variables Y. PLS provides several prediction regions and diagnostic plots as statistical measures. PLS has evolved for coping with the severe demands imposed by complex data structures. In this review article, we outline the algorithms of five advanced PLS techniques and provide some representative examples of each. The selected models are Nonlinear PLS, Multiway PLS, Hierarchical PLS, Orthogonal PLS, and Bi-modal PLS. Studies of particular aspects of living cells (such as the set of genes or proteins in the cell and their interactions) are collectively known as the -omics fields. Omics integrate heterogeneous scientific disciplines and include chemogenomics, proteomics, and metabolomics. The datasets produced within the omics fields are numerous, megavariate and extremely complex. The data structures are frequently incomplete, noisy, nonlinear and collinear demanding modern and powerful multivariate data analysis methods. In particular, the omics technologies have steered biology towards the adoption of orthogonal PLS. We also describe future prospects for the use of PLS algorithms in the omics fields.

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“…Hasegawa et al. conducted QSAR studies using a slightly modified QPLS algorithm 37. The algorithm was revised further by Baffi et al 38…”

Section: Methodsmentioning

confidence: 99%

“…To assess the performance of their modified QPLS, Hasegawa et al. applied the algorithm to 12 phenyl alkylamines with monoamine oxidase (MAO) inhibitory activities 37. The data consisted of one in vitro MAO activity and three in vivo MAO activities.…”

Section: Methodsmentioning

confidence: 99%

Evolution of PLS for Modeling SAR and omics Data

Hasegawa¹,

Funatsu

2012

Molecular Informatics

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“…Twenty phenyl alkylamine derivatives (Fig. 6 ), taken from literature, all having four MAO inhibitory activities, [53], were studied by Hasegawa et al [54] through QSAR analysis. The first biological activity was in vitro MAO inhibitory activity.…”

Section: Monoamine Oxidasementioning

confidence: 99%

Computer-aided Drug Design Applied to Parkinson Targets

Ishiki

Barbosa-Filho

Silva

et al. 2018

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“…A similar set of phenyl alkylamines was studied by Hasegawa et al [87]. The authors developed different models through nonlinear partial least squares where the in vitro and in vivo MAO inhibitory activities were analyzed.…”

Section: Prediction Of Mao Activity Through 2d Qsarmentioning

confidence: 99%

Predicting Monoamine Oxidase Inhibitory Activity Through Ligand-Based Models

Vilar

Ferino²,

Quezada³

et al. 2012

Current Topics in Medicinal Chemistry

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The evolution of bio- and cheminformatics associated with the development of specialized software and increasing computer power has produced a great interest in theoretical in silico methods applied in drug rational design. These techniques apply the concept that “similar molecules have similar biological properties” that has been exploited in Medicinal Chemistry for years to design new molecules with desirable pharmacological profiles. Ligand-based methods are not dependent on receptor structural data and take into account two and three-dimensional molecular properties to assess similarity of new compounds in regards to the set of molecules with the biological property under study. Depending on the complexity of the calculation, there are different types of ligand-based methods, such as QSAR (Quantitative Structure-Activity Relationship) with 2D and 3D descriptors, CoMFA (Comparative Molecular Field Analysis) or pharmacophoric approaches. This work provides a description of a series of ligand-based models applied in the prediction of the inhibitory activity of monoamine oxidase (MAO) enzymes. The controlled regulation of the enzymes’ function through the use of MAO inhibitors is used as a treatment in many psychiatric and neurological disorders, such as depression, anxiety, Alzheimer’s and Parkinson’s disease. For this reason, multiple scaffolds, such as substituted coumarins, indolylmethylamine or pyridazine derivatives were synthesized and assayed toward MAO-A and MAO-B inhibition. Our intention is to focus on the description of ligand-based models to provide new insights in the relationship between the MAO inhibitory activity and the molecular structure of the different inhibitors, and further study enzyme selectivity and possible mechanisms of action.

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Nonlinear Partial Least Squares Modeling of Phenyl Alkylamines with the Monoamine Oxidase Inhibitory Activities

Cited by 21 publications

References 17 publications

Evolution of PLS for Modeling SAR and omics Data

Evolution of PLS for Modeling SAR and omics Data

Computer-aided Drug Design Applied to Parkinson Targets

Predicting Monoamine Oxidase Inhibitory Activity Through Ligand-Based Models

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