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Artificial Intelligence in Drug Design

Overview of attention for book
Cover of 'Artificial Intelligence in Drug Design'

Table of Contents

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    Book Overview
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    Chapter 1 Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges
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    Chapter 2 Machine Learning Applied to the Modeling of Pharmacological and ADMET Endpoints
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    Chapter 3 Fighting COVID-19 with Artificial Intelligence
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    Chapter 4 Application of Artificial Intelligence and Machine Learning in Drug Discovery
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    Chapter 5 Deep Learning and Computational Chemistry
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    Chapter 6 Has Artificial Intelligence Impacted Drug Discovery?
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    Chapter 7 Network-Driven Drug Discovery
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    Chapter 8 Predicting Residence Time of GPCR Ligands with Machine Learning.
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    Chapter 9 De Novo Molecular Design with
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    Chapter 10 Deep Neural for QSAR
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    Chapter 11 Deep Learning in
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    Chapter 12 Deep Learning Applied to Ligand-Based
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    Chapter 13 Ultrahigh Throughput Protein–Ligand with Deep Learning
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    Chapter 14 Artificial Intelligence and Quantum as the Next Pharma Disruptors
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    Chapter 15 Artificial Intelligence in Compound Design
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    Chapter 16 Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases
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    Chapter 17 Artificial Intelligence–Enabled of Novel Compounds that Are Synthesizable
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    Chapter 18 Machine Learning from Omics Data.
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    Chapter 19 Deep Learning in Therapeutic
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    Chapter 20 Machine Learning for Prediction
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    Chapter 21 Opportunities and Considerations in the Application of Artificial Intelligence to Pharmacokinetic Prediction
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    Chapter 22 Artificial Intelligence in Drug Safety and Metabolism
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    Chapter 23 Molecule Ideation Using Matched Molecular
Attention for Chapter 19: Deep Learning in Therapeutic
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Chapter title
Deep Learning in Therapeutic
Chapter number 19
Book title
Methods in Molecular Biology
Published in
Methods in molecular biology, November 2021
DOI 10.1007/978-1-0716-1787-8_19
Pubmed ID
Book ISBNs
978-1-07-161786-1, 978-1-07-161787-8
Authors

Shaver, Jeremy M., Smith, Joshua, Amimeur, Tileli

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 3 16%
Student > Ph. D. Student 3 16%
Student > Doctoral Student 1 5%
Other 1 5%
Student > Bachelor 1 5%
Other 0 0%
Unknown 10 53%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 5 26%
Chemical Engineering 2 11%
Agricultural and Biological Sciences 1 5%
Computer Science 1 5%
Immunology and Microbiology 1 5%
Other 0 0%
Unknown 9 47%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 04 November 2021.
All research outputs
#21,391,516
of 23,884,161 outputs
Outputs from Methods in molecular biology
#10,313
of 13,519 outputs
Outputs of similar age
#364,785
of 430,023 outputs
Outputs of similar age from Methods in molecular biology
#268
of 389 outputs
Altmetric has tracked 23,884,161 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,519 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 430,023 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 389 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.