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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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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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Clinical development focuses on how the drug will interact with the human body and encompasses four key phases of clinical trials, each serving a specific purpose in assessing the safety and effectiveness of new drugs. These phases overlap and build upon one another. Phase I involves a small group of healthy volunteers (typically 20-80 individuals) or, in cases where significant toxicity is expected, patients with the targeted disease, such as cancer or AIDS. The volunteers are tested for...
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AI in drug development: a multidisciplinary perspective.

Víctor Gallego1, Roi Naveiro1, Carlos Roca2

  • 1Institute of Mathematical Sciences (ICMAT-CSIC), Nicolás Cabrera 13-15, 28049, Madrid, Spain.

Molecular Diversity
|July 12, 2021
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) offers innovative solutions to streamline the lengthy and costly drug development process. This review highlights AI

Keywords:
Artificial intelligenceBayesian methodsChemoinformaticsDecision supportDeep learningDrug developmentMachine learning

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Area of Science:

  • Computational chemistry
  • Drug discovery and development
  • Artificial intelligence in medicine

Background:

  • Drug development is a lengthy, expensive, and high-risk process.
  • Innovative technologies like artificial intelligence (AI) are crucial for improving efficiency.
  • Current AI applications span all stages of drug development, from discovery to post-marketing.

Purpose of the Study:

  • To bridge the gap between AI methodologies and drug development processes for chemists and mathematicians.
  • To provide a mathematical perspective on AI tools used in drug discovery.
  • To highlight the practical application of AI in decision-making during drug development.

Main Methods:

  • Review of various artificial intelligence techniques applied to drug development.
  • Focus on Bayesian approaches and their role in molecular modeling.
  • Analysis of AI's contribution to identifying molecular targets, hit/lead compounds, synthesis, and ADME-Tox prediction.

Main Results:

  • AI significantly aids in identifying molecular targets and optimizing compounds.
  • AI tools are effective in predicting Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADME-Tox).
  • Bayesian methods offer unique advantages in molecular modeling within drug development.

Conclusions:

  • AI integration accelerates and de-risks the drug development pipeline.
  • A synergistic approach combining AI expertise with drug development knowledge is essential.
  • AI, particularly Bayesian methods, shows great promise for supporting critical decisions in bringing new drugs to market.