Predicting DPP-IV inhibitors with machine learning approaches.

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Predicting DPP-IV inhibitors with machine learning approaches.

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2017 nî lūn-bûn

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2017年の論文

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2017年学术文章

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2017年学术文章

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2017年学术文章

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2017年学术文章

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2017年学术文章

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2017年學術文章

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2017年學術文章

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2017年學術文章

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name

Predicting DPP-IV inhibitors with machine learning approaches.

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Predicting DPP-IV inhibitors with machine learning approaches.

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type

Item

label

Predicting DPP-IV inhibitors with machine learning approaches.

@ast

Predicting DPP-IV inhibitors with machine learning approaches.

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Predicting DPP-IV inhibitors with machine learning approaches.

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Predicting DPP-IV inhibitors with machine learning approaches.

@en

P1476

Predicting DPP-IV inhibitors with machine learning approaches

@en

P2093

Chanjuan Li

http://www.w3.org/2001/XMLSchema#string

Jie Cai

http://www.w3.org/2001/XMLSchema#string

Jiewen Du

http://www.w3.org/2001/XMLSchema#string

Jiming Ye

http://www.w3.org/2001/XMLSchema#string

Zhihong Liu

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P2860

Crystal structure of human dipeptidyl peptidase IV/CD26 in complex with a substrate analog

Fused bicyclic heteroarylpiperazine-substituted L-prolylthiazolidines as highly potent DPP-4 inhibitors lacking the electrophilic nitrile group

A comparative study of the binding modes of recently launched dipeptidyl peptidase IV inhibitors in the active site

Anagliptin, a potent dipeptidyl peptidase IV inhibitor: its single-crystal structure and enzyme interactions

The incretin system: glucagon-like peptide-1 receptor agonists and dipeptidyl peptidase-4 inhibitors in type 2 diabetes

Predicting mTOR inhibitors with a classifier using recursive partitioning and Naïve Bayesian approaches

Assessing the accuracy of prediction algorithms for classification: an overview

POVME: an algorithm for measuring binding-pocket volumes.

Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and laplacian-modified naive bayesian classifiers.

A new approach to finding natural chemical structure classes.

P2888

s10822-017-0009-6

P304

393-402

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scholarly article

P356

10.1007/S10822-017-0009-6

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P433

http://www.w3.org/2001/XMLSchema#string

P478

http://www.w3.org/2001/XMLSchema#string

P50

Qiong Gu

Jun Xu

P577

2017-02-02T00:00:00Z

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P698

28155089

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P921

machine learning