Assessment of the classification abilities of the CNS multi-parametric optimization approach by the method of logistic regression.

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Assessment of the classification abilities of the CNS multi-parametric optimization approach by the method of logistic regression.

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2016 nî lūn-bûn

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2016年の論文

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2016年学术文章

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2016年学术文章

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2016年学术文章

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2016年学术文章

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2016年学术文章

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2016年学术文章

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2016年學術文章

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2016年學術文章

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Assessment of the classificati ...... method of logistic regression.

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Assessment of the classificati ...... method of logistic regression.

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Assessment of the classificati ...... method of logistic regression.

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Assessment of the classificati ...... method of logistic regression.

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Assessment of the classificati ...... method of logistic regression.

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Assessment of the classificati ...... method of logistic regression.

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P1476

Assessment of the classificati ...... method of logistic regression.

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P2093

A Mukhametov

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D E Polianczyk

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O A Raevsky

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V Y Grigorev

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P2860

Knowledge-Based, Central Nervous System (CNS) Lead Selection and Lead Optimization for CNS Drug Discovery

Fast Calculation of Molecular Polar Surface Area as a Sum of Fragment-Based Contributions and Its Application to the Prediction of Drug Transport Properties

Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings

Multi-objective optimization methods in drug design.

QSAR classification model for antibacterial compounds and its use in virtual screening.

Systematic drug repositioning for a wide range of diseases with integrative analyses of phenotypic and molecular data.

CNS drug design: balancing physicochemical properties for optimal brain exposure.

Moving beyond rules: the development of a central nervous system multiparameter optimization (CNS MPO) approach to enable alignment of druglike properties.

Development of a new physicochemical model for brain penetration and its application to the design of centrally acting H2 receptor histamine antagonists.

Using information from historical high-throughput screens to predict active compounds.

P304

629-635

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P31

scholarly article

P356

10.1080/1062936X.2016.1212922

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P433

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P478

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P577

2016-08-01T00:00:00Z

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P698

27477321

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P921

logistic regression model