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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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Drugs, the chemical agents used in diagnosing, treating, or preventing diseases, undergo a four-phase process of development: pharmaceutic, pharmacokinetics, pharmacodynamics, and therapeutic.
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Prescription drugs require a prescription from a medical practitioner and can only be obtained from a pharmacy. They have many applications, including treating pain, anxiety, and hypertension.
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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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Microorganisms play a fundamental role in vaccine development, gene therapy, and therapeutic production. Their biological properties are harnessed to advance medicine and public health. Beyond immunization, microorganisms contribute to gut health, antibiotic synthesis, and genetic disease treatment.Live Attenuated and Inactivated VaccinesLive attenuated vaccines, such as the measles, mumps, and rubella (MMR) vaccine, utilize weakened forms of pathogens to closely resemble natural infections.
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Learning to discover medicines.

Minh-Tri Nguyen1, Thin Nguyen1, Truyen Tran1

  • 1Applied Artificial Intelligence Institute, Deakin University, Burwood, VIC Australia.

International Journal of Data Science and Analytics
|November 28, 2022
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) offers new hope for accelerating drug discovery by tackling complex biomedical challenges. This review explores AI methodologies in molecular representation, data-driven predictions, and knowledge graph reasoning for novel medicine development.

Keywords:
Artificial intelligenceBiomedical representation learningDrug discoveryDrug discovery reasoningMachine learning

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Area of Science:

  • Computational chemistry
  • Bioinformatics
  • Artificial intelligence

Background:

  • Drug discovery is crucial for human longevity but faces challenges due to unexplored biomedical space.
  • The pace of discovering novel therapeutics has slowed, necessitating innovative approaches.
  • Modern artificial intelligence (AI) presents a promising solution to accelerate the drug discovery pipeline.

Purpose of the Study:

  • To review recent advancements in AI methodologies for drug discovery.
  • To organize the rapidly growing literature on AI applications in medicine development.
  • To identify future research directions and open challenges in the field.

Main Methods:

  • Representation learning for molecular sequences and graphs.
  • Data-driven reasoning for predicting molecular properties and optimizing compounds.
  • Knowledge-based reasoning using biomedical knowledge graphs.

Main Results:

  • AI methodologies are categorized into representation learning, data-driven reasoning, and knowledge-based reasoning.
  • AI enables prediction of molecular properties, binding affinities, and *de novo* molecule generation.
  • AI facilitates the planning of synthesis pathways and reasoning over complex biomedical data.

Conclusions:

  • AI is rapidly maturing and poised to significantly impact drug discovery.
  • The review provides a structured overview of AI techniques applied to medicine development.
  • Future research should focus on addressing identified challenges and exploring new AI applications.