Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

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Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

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2018 nî lūn-bûn

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2018年の論文

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年学术文章

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2018年學術文章

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2018年學術文章

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Comparing and Validating Machi ...... m tuberculosis Drug Discovery.

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Comparing and Validating Machi ...... m tuberculosis Drug Discovery.

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label

Comparing and Validating Machi ...... m tuberculosis Drug Discovery.

@en

Comparing and Validating Machi ...... m tuberculosis Drug Discovery.

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Comparing and Validating Machi ...... m tuberculosis Drug Discovery.

@en

Comparing and Validating Machi ...... m tuberculosis Drug Discovery.

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P1476

Comparing and Validating Machi ...... m tuberculosis Drug Discovery.

@en

P2093

Alex M Clark

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Alexander L Perryman

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Alexandru Korotcov

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Daniel P Russo

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Joel S Freundlich

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Kimberley M Zorn

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Robert C Reynolds

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Thomas Lane

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Valery Tkachenko

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P2860

Thousands of chemical starting points for antimalarial lead identification

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Raising awareness of the importance of funding for tuberculosis small-molecule research

The ChEMBL bioactivity database: an update

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

A virtual screen discovers novel, fragment-sized inhibitors of Mycobacterium tuberculosis InhA

Learning from the past for TB drug discovery in the future

New target prediction and visualization tools incorporating open source molecular fingerprints for TB Mobile 2.0

The Chemical Validation and Standardization Platform (CVSP): large-scale automated validation of chemical structure datasets

Mutations in pncA, a gene encoding pyrazinamidase/nicotinamidase, cause resistance to the antituberculous drug pyrazinamide in tubercle bacillus

P31

scholarly article

P356

10.1021/ACS.MOLPHARMACEUT.8B00083

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P407

English

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Sean Ekins

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2018-04-19T00:00:00Z

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P698

29672063

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P921

Mycobacterium tuberculosis

machine learning

P932

6167198

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