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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
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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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基基:知识导向图表 自主监督学习以提高分子性质预测.

Van-Thinh To1, Phuoc-Chung Van Nguyen1, Gia-Bao Truong1

  • 1Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, 41 Dinh Tien Hoang, District 1, Ho Chi Minh City 700000, Vietnam.

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这项研究引入了用于分子性质预测的知识导向图 (KGG) 框架. KGG使用具有轨道特征的自我监督学习,有效地增强图形神经网络 (GNN) 模型,即使数据有限.

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科学领域:

  • 计算化学是一种计算化学.
  • 机器学习在药物发现中的作用
  • 材料科学 信息学 信息学

背景情况:

  • 图形神经网络 (GNN) 在分子表示学习方面表现有前途.
  • 挑战包括数据稀缺性和有限的概括性,因为昂贵的标签数据采集.
  • 当前的GNN通常缺乏全面的化学领域知识,例如轨道信息,在它们的初始特征中.

研究的目的:

  • 为了解决GNN中数据稀缺性和特征限制,用于分子性质预测.
  • 引入一个新的知识导向图 (KGG) 框架.
  • 使用轨道信息的特征提高分子性质预测的效率和准确性.

主要方法:

  • 开发了一个知识导向图 (KGG) 框架,利用自我监督学习.
  • 预训练模型使用轨道级特征来减少对广泛标记数据集的依赖.
  • 为原子杂交和结合轨道接触的键类型提出了新的表示方法.
  • 在ZINC15数据集中的大约25万个分子上使用了具有成本效益的预训练策略.

主要成果:

  • 在各种下游分子性质预测任务上,KGG框架显著超过了最先进的基线.
  • 证明了数据效率,与当代方法相比,预训练需要更少的分子.
  • t-SNE可视化和与传统分子指纹的比较验证了该方法的有效性和稳定性.

结论:

  • 通过轨道信息表示,KGG框架提供了数据效率和架构多功能性.
  • 它有效地从适度的数据集中提取化学知识,避免了广泛的预训练.
  • 该方法在低数据微调方面表现出色,为药物发现和材料科学中的各种GNN架构提供了坚实的基础.