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人工智能 (AI) 在药物发现的分子性质预测方面取得了有限的成功. 数据集的大小对于表示学习模型的良好表现至关重要.

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科学领域:

  • 计算化学是一种计算化学.
  • 化学信息学 化学信息学
  • 药物发现 药物发现

背景情况:

  • 人工智能 (AI) 在药物发现中越来越多地用于分子性质预测.
  • 分子表示学习技术已经进步,但它们在预测中的潜在机制仍然不清楚.
  • 这阻碍了人工智能驱动的药物发现的进展.

研究的目的:

  • 通过使用多样化的表示,广泛评估用于分子性质预测的AI模型.
  • 调查不同数据集大小的模型性能,包括低数据场景.
  • 确定影响预测准确度和模型局限性的关键因素.

主要方法:

  • 通过固定表示,SMILES序列和分子图表训练了62,820个模型.
  • 在MoleculeNet上评估模型,与阿片类药物相关的和额外的活动数据集.
  • 通过使用不同大小的数据集,在低数据和高数据制度中评估预测能力.

主要成果:

  • 代表性学习模型在大多数分子性质预测任务上表现有限.
  • 一些关键因素被确定为显著影响预测评估结果的关键因素.
  • 发现活动悬崖在很大程度上影响了模型预测.
  • 模型性能与数据集大小有很强的相关性,更大的数据集对于表示学习至关重要.

结论:

  • 当前的代表性学习模型在分子性质预测方面表现出有限的有效性.
  • 数据集大小是代表性学习在这个领域的成功的一个关键决定因素.
  • 需要进一步的研究来理解和克服AI模型在药物发现中的局限性.