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相关概念视频

Predicting Molecular Geometry02:27

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When proton-coupled carbon-13 spectra are simplified by a broadband proton decoupling technique, structural information about the coupled protons is lost. Distortionless enhancement by polarization transfer (DEPT) is a technique that provides information on the number of hydrogens attached to each carbon in a molecule. While the DEPT experiment utilizes complex pulse sequences, the pulse delay and flip angle are specifically manipulated. The resulting signals have different phases depending on...
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In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
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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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In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
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Author Spotlight: Streamlining Protein Target Prediction and Validation via Molecular Docking and CETSA
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使用基于变压器的方法预测分子描述器属性.

Tuan Tran1, Chinwe Ekenna1

  • 1Department of Computer Science, University at Albany, Albany, NY 12203, USA.

International journal of molecular sciences
|August 12, 2023
PubMed
概括

本研究介绍了半监督机器学习模型,用于使用SMILES字符串预测分子性质. 该方法实现了最先进的性能,即使使用了用于药物发现的基于3D结构的新注意力机制.

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
  • 化学信息学 化学信息学

背景情况:

  • 预测分子性质对于药物发现至关重要.
  • 现有的方法通常需要大型标记数据集.
  • 机器学习为高效的财产预测提供了潜力.

研究的目的:

  • 开发和评估用于预测分子性质的半监督机器学习模型.
  • 为了提高模型培训,利用标记和未标记的数据.
  • 探索有效的注意力机制用于药物候选人的预测.

主要方法:

  • 一个两阶段的方法:在SMILES字符串上使用掩盖语言模型进行预训练,并在下游任务上进行微调.
  • 使用标记和未标记的SMILES字符串的大数据集.
  • 为端到端的变压器模型开发基于3D结构的新型注意力评分.

主要成果:

  • 拟议的半监督模型在MoleculeNet任务上实现了与最先进的方法可比的性能.
  • 新的基于3D结构的注意力方法使得预训练模型的性能与预训练模型相比,计算成本降低.
  • 该模型成功地预测了抗疟疾药物候选者.
关键词:
原菌 (Plasmodium falciparum) 是一种有毒的病毒.这是一次大规模的培训.机器学习是机器学习.分子性质分子性质的分子性质.变压器 变压器

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结论:

  • 半监督学习有效地预测分子特性,并有助于药物发现.
  • 整合3D结构信息提供了计算效率,而不会牺牲性能.
  • 开发的模型代表了化学信息学机器学习的重大进步.