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In silico machine learning methods in drug development

Current Topics in Medicinal Chemistry

Sep 30, 2014

Dimitar DobchevMati Karelson

PMID: 25262800

Abstract

Machine learning (ML) computational methods for predicting compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties are being increasingly applied in drug discovery and evaluation. Recently, mac...read more

Mentioned in this Paper

Metabolic Process, Cellular
Drug Development
Genetic Programming
Neural Network Simulation
Knowledge Representation (Computer)
Body Excretions
Metabolic Pathway
Quantitative Structure Property Relationship
Excretory Function
Pharmacodynamics
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In silico machine learning methods in drug development

Current Topics in Medicinal Chemistry

Sep 30, 2014

Dimitar DobchevMati Karelson

PMID: 25262800

DOI:

Abstract

Machine learning (ML) computational methods for predicting compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties are being increasingly applied in drug discovery and evaluation. Recently, mac...read more

Mentioned in this Paper

Metabolic Process, Cellular
Drug Development
Genetic Programming
Neural Network Simulation
Knowledge Representation (Computer)
Body Excretions
Metabolic Pathway
Quantitative Structure Property Relationship
Excretory Function
Pharmacodynamics

Similar Papers Found In These Feeds

Drug-Induced Diseases

Drug-induced diseases (DID) also called as iatrogenic diseases. Most of these DIDs are largely preventable, if strict vigilance and proper periodic clinical and diagnostic monitoring are undertaken

Neural Networks

Here is the latest research on physiological and in silico networks and circuits implicated in the nervous system.

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Paper Details
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  • References
  • Citations12
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    References currently unavailable

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  • Citations12
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