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Related Concept Videos

Drug Discovery: Overview01:26

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Drug discovery is a multifaceted process involving extensive screening, testing, and optimization of lead compounds to identify potential new drugs for therapeutic use. It combines several approaches, including screening large numbers of natural products, chemical modification of known active molecules, identification of new drug targets, and rational design based on biological mechanisms and drug-receptor structure. These approaches are carried out in both academic research laboratories and...
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Drug design is a dynamic field that involves discovering and developing new medications based on specific biological targets. This process heavily relies on structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) to guide the design and optimization of efficient drugs.
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Advances in genomics have profoundly influenced drug discovery by increasing both the speed and accuracy of pharmaceutical development. Pharmacogenomics, which examines how genetic variation influences drug response, facilitates the identification of novel therapeutic targets and enables patient stratification for personalized treatment. These strategies contribute to improved drug efficacy, minimized adverse effects, and more efficient clinical trial design.Mapping genetic differences...
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Drug elimination refers to drug removal from the body, either through urine or bile, by the kidneys or liver, respectively. A pharmacokinetic parameter, drug clearance, measures the efficiency of drug removal from the bloodstream within a specific time frame. It is calculated as the rate at which a drug is eliminated from plasma divided by the drug's concentration in plasma.
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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
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Preclinical development consists of a series of tests that ensure the safety and efficacy of a new therapeutic compound before it is tested in humans. There are four main phases to this process. First, safety pharmacology tests are conducted to ensure the drug does not produce any acutely harmful effects. These tests examine parameters such as bronchoconstriction, cardiac dysrhythmias, blood pressure changes, and ataxia. Next, preliminary toxicological testing is performed to determine the...
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Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
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Deep Learning in Drug Discovery.

Erik Gawehn1, Jan A Hiss1, Gisbert Schneider2

  • 1Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, CH-8093 Zurich, Switzerland, Fax: +41 44 633 13 79, Tel: +41 44 633 74 38.

Molecular Informatics
|August 6, 2016
PubMed
Summary
This summary is machine-generated.

Deep learning, using advanced artificial neural networks, is gaining traction in molecular informatics and drug discovery. These methods offer promising tools for computer-assisted drug design and discovery.

Keywords:
bioinformaticscheminformaticsdrug designmachine-learningneural networkvirtual screening

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Artificial intelligence in medicine

Background:

  • Artificial neural networks (ANNs) were previously significant in molecular informatics and drug discovery.
  • There is a resurgence of interest in applying advanced deep learning architectures to pharmaceutical research.

Purpose of the Study:

  • To provide an overview of the emerging field of molecular informatics utilizing deep learning.
  • To present fundamental concepts of prominent deep learning methods.
  • To motivate the use of these techniques in computer-assisted drug discovery and design.

Main Methods:

  • Focus on deep neural networks.
  • Explanation of restricted Boltzmann machine networks.
  • Discussion of convolutional neural networks.

Main Results:

  • The application of deep learning in drug discovery is currently limited compared to other life sciences.
  • Deep learning methods offer novel approaches for molecular informatics.

Conclusions:

  • Deep learning techniques show potential for advancing computer-assisted drug discovery.
  • Further exploration of deep neural networks, restricted Boltzmann machines, and convolutional networks is encouraged for pharmaceutical research.