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相关概念视频

Colloidal precipitates01:09

Colloidal precipitates

546
The high insolubility of some precipitates can result in an unfavorable relative supersaturation. This can lead to colloidal particles with a large surface-to-mass ratio, where adsorption is promoted. For instance, in the precipitation of silver chloride, silver ions are adsorbed on the surface of the colloidal particles, forming a primary layer. This layer attracts ions of opposite charge (such as nitrate ions), forming a diffuse secondary layer of adsorbed ions. This electric double layer...
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Colloids03:22

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Children at play often make suspensions such as mixtures of mud and water, flour and water, or a suspension of solid pigments in water known as tempera paint. These suspensions are heterogeneous mixtures composed of relatively large particles that are visible to the naked eye or can be seen with a magnifying glass. They are cloudy, and the suspended particles settle out after mixing. On the other hand, a solution is a homogeneous mixture in which no settling occurs and in which the dissolved...
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Coagulation01:06

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Colloidal solids are solid particles suspended in solution. They are usually negatively charged, attracting a compact primary layer of positively charged ions, which attract more counterions to form an electrical double layer. Electrostatic repulsion between the charged double layers prevents the particles from colliding, stabilizing the colloids. These solids are often undesirable because they can contain toxins that are difficult to remove. Coagulation is a technique that helps aggregate and...
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相关实验视频

Updated: Jun 21, 2025

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence
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使用AI/ML模型对体分子聚合的预测.

David C Kombo1, J David Stepp1, Sungtaek Lim1

  • 1Integrated Drug Discovery, Sanofi, 350 Water St., Cambridge, Massachusetts 02141, United States.

ACS omega
|July 8, 2024
PubMed
概括

人工智能/ML模型预测小分子聚合,有助于药物发现查. 朴素贝叶斯和深度神经网络在识别非聚合化合物以进行图书馆选择方面表现出卓越的性能.

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Last Updated: Jun 21, 2025

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科学领域:

  • 计算化学的计算化学
  • 药物发现 药物发现 药物发现
  • 机器学习 机器学习

背景情况:

  • 小有机分子的体聚合在药物发现查中提出了挑战.
  • 需要预测模型来有效地分类选结果,并优化化学库.

研究的目的:

  • 开发和验证AI/ML模型,用于预测小型有机分子的体聚合.
  • 确定关键的分子描述符和与聚合倾向相关的化学特征.
  • 在药物发现中应用预测模型来对未来的化学图书馆进行分类.

主要方法:

  • 利用了各种AI/ML技术,包括天真贝叶斯式,深度神经网络,逻辑回归,递归分区树,支持向量机器和随机森林.
  • 在实验观察的小有机分子数据集上训练和测试模型.
  • 采用了支架树分析和匹配分子对分析 (MMPA) 来识别聚合驱动特征.

主要成果:

  • 纯粹贝叶斯式和深度神经网络表现出最低的平衡错误率 (BER),优于其他方法.
  • 模型成功地区分了聚合和非聚合分子.
  • 确定了疏水性,分子量,可溶性,sp3碳原子 (Fsp3) 分数和基组 (ES_Sum_sOH) 的电形态状态作为关键描述符.
  • 突出了高Fsp3值的支架在防止聚合方面的作用.

结论:

  • 人工智能/ML模型,特别是天真贝叶斯和深度神经网络,对于预测体聚合是有效的.
  • 对于设计非聚合分子来说,Fsp3值和特定的化学支架很重要.
  • 这些模型的未来应用增强了化学库选择和高吞吐量选 (HTS) 的多样性.