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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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The receptor occupancy theory connects a drug's response to the number of occupied receptors. With higher drug concentrations, more receptors are occupied, leading to increased responses. The formation of drug-receptor complexes involves association and dissociation rates, which reach equilibrium when the forward and backward reactions are equal. The equilibrium association constant (Ka) and its inverse, the equilibrium dissociation constant (Kd), indicate drug affinity. Higher Ka and lower...
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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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From traditional to data-driven medicinal chemistry: A case study.

Ryo Kunimoto1, Jürgen Bajorath2, Kazumasa Aoki3

  • 1Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, D-53113 Bonn, Germany; Medicinal Chemistry Research Laboratories, R&D Division, Daiichi Sankyo Company, 140-8710 Tokyo, Japan.

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Artificial intelligence (AI) and data science are making an impact in drug discovery. A pilot study demonstrated data-driven medicinal chemistry

Keywords:
ChemoinformaticsData scienceData-driven R&DDrug discoveryMedicinal chemistry

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Area of Science:

  • Drug discovery
  • Medicinal chemistry
  • Data science

Background:

  • Artificial intelligence (AI) and data science are increasingly influencing drug discovery.
  • Transitioning computational approaches from concept to practical application in drug discovery is challenging.
  • Quantifying the impact of these technologies on drug discovery projects requires careful study.

Purpose of the Study:

  • To integrate data science into practical medicinal chemistry workflows.
  • To quantify the measurable impact of data-driven approaches in early-phase drug discovery.
  • To explore new training models for medicinal chemists in pharmaceutical companies.

Main Methods:

  • Implementation of a pilot study integrating data science tools within medicinal chemistry.
  • Quantitative assessment of the impact of these integrated approaches on drug discovery projects.
  • Analysis of variations in early-phase drug discovery focal points across pharmaceutical companies.

Main Results:

  • The pilot study indicated significant potential for data-driven medicinal chemistry.
  • Measurable impact was demonstrated, validating the integration of data science.
  • The study highlighted the need for new internal training models for chemists.

Conclusions:

  • Data-driven medicinal chemistry shows substantial promise for enhancing drug discovery.
  • Successful integration of data science can yield quantifiable benefits.
  • New training paradigms are suggested for developing next-generation medicinal chemists.