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Genetic Screens

Genetic Screens

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.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...

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Updated: Jan 13, 2026

Cell Surface Receptor Identification Using Genome-Scale CRISPR/Cas9 Genetic Screens

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克拉萨瓦-一个专家系统用于虚拟选克拉斯G12D抑制剂的虚拟查.

Oleg V Tinkov¹, Pavel E Gurevich^2,1, Sergei A Nikolenko¹

¹Ligand Pro, Moscow 121205, Russia.

International journal of molecular sciences

|January 10, 2026

概括

我们为KRAS G12D抑制剂开发了一种新的QSAR模型,改进了药物发现. 我们的KRASAVA平台预测活动,确保安全性,并确定癌症治疗的有前途的新药候选药物.

关键词:

在QSAR中使用QSAR.在RDKitit中使用.机器学习是机器学习.分子对接的分子对接.结构性解释是结构性的解释.

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Last Updated: Jan 13, 2026

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科学领域:

药用化学医学化学
计算化学计算化学
药理学药理学是指药理学的学科.

背景情况:

克拉斯G12D抑制剂对于癌症治疗至关重要.
现有的定量结构-活动关系 (QSAR) 模型缺乏适用性领域确定和虚拟选.
这限制了它们在药物发现和开发中的实用性.

研究的目的:

为KRAS G12D抑制剂开发新的QSAR模型.
将这些模型集成到一个用户友好的计算框架中.
识别和验证潜在的新型KRAS G12D抑制剂候选药物.

主要方法:

采用各种分子描述器和机器学习算法来构建回归QSAR模型.
开发了一个具有高预测精度 (Q2=0.70) 的共识模型,并定义了适用性领域.
将模型集成到KRASAVA Python框架中,用于活动预测,基于规则的过和分子对接 (GNINA).

主要成果:

在外部测试组上,共识QSAR模型实现了0.70的Q2测试值.
克拉萨瓦平台成功预测了抑制活性,评估了生物可用性规则,并确定了不必要的化学结构.
分子对接研究验证了拟议的抑制剂,显示与QSAR预测和参考化合物MRTX1133 (RMSD 0.76 Å) 的一致性.

结论:

开发的QSAR模型和KRASAVA框架为KRAS G12D抑制剂的发现提供了一个强大的工具.
该研究成功地确定了具有潜在治疗应用的有希望的新型抑制剂候选者.
这些发现突显了QSAR建模和分子对接之间的协同作用,以实现高效的药物设计.