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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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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
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相关实验视频

Updated: Jul 11, 2025

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人工智能:用于选大型数据库和药物发现的机器学习方法.

Prachi P Parvatikar1, Sudha Patil2, Kedar Khaparkhuntikar3

  • 1Department of Biotechnology, Allied Health Science, BLDE (Deemed-to-be University), Vijayapur 586103, Karnataka, India.

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概括

人工智能 (AI) 和机器学习 (ML) 通过选大型数据库来加速药物发现. 本综述探讨了虚拟查中的AI/ML,重点关注抗病毒开发的优势和挑战.

关键词:
在ADME/T的深度学习 (DL) 中.算法算法是一种算法.抗病毒数据库的数据库.人工神经网络 (ANN) 是一个人工神经网络.虚拟查 (VS) 是指虚拟的查.

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科学领域:

  • 计算化学是一种计算化学.
  • 药物发现 药物发现
  • 生物信息学是一种生物信息学.

背景情况:

  • 药物发现面临着疾病爆发和药物耐药性等挑战.
  • 虚拟查是从大型分子数据库中识别药物点的关键计算机辅助方法.
  • 深度学习和人工神经网络显著提升了药物发现过程.

研究的目的:

  • 审查人工智能 (AI) 和机器学习 (ML) 在大型分子数据库的虚拟选中的应用.
  • 突出AI/ML技术在药物发现中的优点,缺点和潜在危害,重点关注抗病毒药物和传染病.

主要方法:

  • 对AI/ML在药物发现和虚拟查中的应用进行现有文献的审查.
  • 分析AI/ML工具及其在各种药物发现过程中的使用.
  • 讨论AI/ML工具从非传染性疾病研究转化为传染性疾病研究.

主要成果:

  • 人工智能/ML算法已经现代化了各种药物发现阶段,包括虚拟查,毒性预测和药物重新定位.
  • 许多网络服务器和AI/ML工具可用于选化学数据库.
  • 应用AI/ML工具存在挑战,主要是针对非传染性疾病开发的工具,以发现传染病治疗方法.

结论:

  • 通过虚拟查,AI/ML为加速识别新型抗病毒和其他治疗剂提供了显著的潜力.
  • 解决AI/ML的局限性和潜在危害对于成功转化为传染病药物发现至关重要.
  • 需要进一步的研究来优化AI/ML方法来应对传染病的挑战.