AIMC Topic: Drug Evaluation, Preclinical

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[Machine Learning-based Prediction of Seizure-inducing Action as an Adverse Drug Effect].

Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan Jan 1, 2018
　During the preclinical research period of drug development, animal testing is widely used to help screen out a drug's dangerous side effects. However, it remains difficult to predict side effects within the central nervous system. Here, we introduce...
Humans Machine Learning Forecasting Animals Drug Discovery Mice Seizures Drug Evaluation, Preclinical Drug-Related Side Effects and Adverse Reactions Diphenhydramine Theophylline Enoxacin Strychnine
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[Challenges to Improve the Prediction Accuracy of the Non-clinical Tests for Human CNS Adverse Effects: Potentials of Artificial Intelligence and Human ESC/iPSC-derived Neurons].

Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan Jan 1, 2018
Humans Artificial Intelligence Drug Discovery Cell Differentiation Drug Evaluation, Preclinical Neurons Central Nervous System Agents Neuroimaging Animal Testing Alternatives Induced Pluripotent Stem Cells Human Embryonic Stem Cells
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Optimized Virtual Screening Workflow: Towards Target-Based Polynomial Scoring Functions for HIV-1 Protease.

Combinatorial chemistry & high throughput screening Jan 1, 2017
BACKGROUND: One key step in the development of inhibitors for an enzyme is the application of computational methodologies to predict protein-ligand interactions. The abundance of structural and ligand-binding information for HIV-1 protease opens up t...
Machine Learning Molecular Docking Simulation Drug Evaluation, Preclinical Ligands HIV Protease HIV Protease Inhibitors
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