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Related Concept Videos

Drug Discovery: Overview01:26

Drug Discovery: Overview

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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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Drugs target macromolecules to modify ongoing cellular processes. Primary drug targets include receptors, ion channels, transporters, and enzymes.
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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
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Drug-receptor interaction describes the binding of receptors by drugs, but not all drug-receptor interactions result in activation and tissue response. For instance, the binding of agonists activates the receptor to generate a cellular reaction, while antagonists bind to receptors without causing their activation.
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Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Progress of AI-Driven Drug-Target Interaction Prediction and Lead Optimization.

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Artificial intelligence (AI) accelerates drug discovery by analyzing molecular structures and predicting drug-target interactions. This review highlights AI

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Area of Science:

  • Pharmaceutical R&D
  • Computational Chemistry
  • Bioinformatics

Background:

  • Drug discovery is a complex and costly process in pharmaceutical research and development.
  • Artificial intelligence (AI) offers powerful tools to enhance various stages of drug discovery and development.
  • AI facilitates molecular feature extraction, drug-target interaction analysis, and disease-relationship modeling.

Purpose of the Study:

  • To review recent advancements in AI applications for drug design.
  • To provide insights into AI-driven strategies for target identification, synthetic accessibility, lead optimization, and ADMET property evaluation.
  • To establish a conceptual framework for implementing AI in pharmaceutical research.

Main Methods:

  • Review of current literature on AI applications in drug discovery.
  • Summarization of deep learning tools and methodologies.
  • Organization of AI-driven strategies for different drug discovery phases.

Main Results:

  • AI improves prediction accuracy, accelerates timelines, and reduces costs in drug discovery.
  • AI applications cover target identification, synthetic accessibility, lead optimization, and ADMET evaluation.
  • Deep learning tools are increasingly important for guiding AI implementation.

Conclusions:

  • AI significantly enhances efficiency and success rates in pharmaceutical R&D.
  • A structured approach to AI implementation is crucial for advancing drug discovery.
  • This review provides a framework for leveraging AI methodologies in pharmaceutical research.