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Related Concept Videos

Structure-Activity Relationships and Drug Design01:28

Structure-Activity Relationships and Drug Design

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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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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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Biopharmaceutical Factors Influencing Drug Product Design: Overview01:22

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Rational drug product design integrates knowledge of the drug’s physicochemical properties, formulation components, manufacturing techniques, and intended route of administration. Each factor influences the drug’s performance, including how it is released, absorbed, and eliminated in the body.The physicochemical properties of a drug—such as solubility, stability, and particle size—affect its compatibility with excipients and the choice of dosage form. Excipients, though...
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
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Determining protein-drug binding can be achieved through indirect and direct methods, each providing valuable insights into the interaction between proteins and drugs.
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Measurement of Bioavailability: Pharmacodynamic Methods01:20

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Pharmacodynamic methods provide insights into a drug's effects on physiological processes over time and play a crucial role in understanding bioavailability and therapeutic efficacy. These methods can be broadly classified into acute pharmacological and therapeutic response approaches, each with distinct mechanisms and applications.The acute pharmacological response method directly correlates a drug's physiological effects, such as ECG or pupil diameter changes, to its time course in the body.
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Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD).

Jai Woo Lee1, Miguel A Maria-Solano1, Thi Ngoc Lan Vu1

  • 1Global AI Drug Discovery Center, College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, Seoul 03760, Republic of Korea.

Biochemical Society Transactions
|January 25, 2022
PubMed
Summary

Computer-aided drug design (CADD) leverages big data and artificial intelligence (AI) to overcome drug development challenges. These computational methods accelerate the identification of drug candidates and prediction of properties.

Keywords:
artificial intelligencebig datacomputer-aided drug designstatisticsstructure-based drug discovery

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Area of Science:

  • Computational Chemistry
  • Bioinformatics
  • Artificial Intelligence in Drug Discovery

Background:

  • Drug design is complex and expensive due to biological intricacies.
  • Computer-aided drug design (CADD) offers efficient solutions to drug development limitations.
  • Advances in big data and AI are transforming CADD methodologies.

Purpose of the Study:

  • To review the application of big data and AI in computer-aided drug design (CADD).
  • To highlight the importance of data pre-processing for AI and big data methods in drug discovery.
  • To discuss the current status of AI and big data in key CADD areas.

Main Methods:

  • Data pre-processing techniques for cleaning and preparing biomedical data.
  • Application of big data analytics and artificial intelligence (AI) algorithms.
  • Integration of computational and statistical methods for drug design.

Main Results:

  • AI and big data methods are applicable to target identification, structure-based virtual screening (SBVS), and ADMET property prediction.
  • Data pre-processing ensures consistent and reproducible results from AI and big data analyses.
  • These approaches enable accurate analysis of large biomedical datasets for predictive modeling.

Conclusions:

  • Big data and AI significantly enhance the accuracy and efficiency of drug design.
  • Understanding biomedical data architectures is crucial in the big data era for drug discovery.
  • CADD, powered by AI and big data, is vital for overcoming drug development hurdles.