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Molecular Dynamics Workflows to Compute Large-Scale Sets of Absolute Binding Free Energies Aiding Drug Candidate and Binding Pose Selection.

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ProfhEX: Empowering Early Drug Discovery with Machine Learning-Based Target Profiling and Liability Prediction.

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Biosensor-based High Throughput Biopanning and Bioinformatics Analysis Strategy for the Global Validation of Drug-protein Interactions
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ProfhEX:基于人工智能的平台,用于小分子责任分析.

Filippo Lunghini1, Anna Fava1, Vincenzo Pisapia2

  • 1EXSCALATE, Dompé Farmaceutici SpA, Via Tommaso de Amicis 95, 80123, Naples, Italy.

Journal of cheminformatics
|June 9, 2023
PubMed
概括
此摘要是机器生成的。

ProfhEX是一个人工智能工具,可以预测药物负债,减少药物发现中的失败. 它对7个毒性组的小分子进行了分析,有助于早期风险评估和节省成本.

关键词:
负债的概况是负债的概况.机器学习 机器学习多种药理学 多种药理学虚拟选是一个虚拟的选.这是一个Web服务.

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科学领域:

  • 计算化学和化学信息学
  • 人工智能在药物发现中的作用
  • 毒理学和安全药理学

背景情况:

  • 非目标药物相互作用导致候选药物开发中的重大失败.
  • 早期预测不良影响至关重要,以尽量减少患者风险,动物试验和成本.
  • 随着化学图书馆规模的不断增加,需要有效的选工具来估计责任.

研究的目的:

  • 介绍ProfhEX,一个人工智能驱动的平台,用于分析小分子.
  • 使用机器学习预测七个关键毒性组的潜在负债.
  • 为早期候选药物评估提供一流的查工具.

主要方法:

  • 开发了46个符合经合组织标准的机器学习模型,使用梯度增强和随机森林算法.
  • 训练模型对46个目标中的210,116种独特化合物的实验亲和数据进行训练.
  • 严格使用内部 (交叉验证,引导,y-scrambling) 和外部数据集验证模型.

主要成果:

  • 冠军模型实现了0.84的平均皮尔森相关系数和0.68.2的R2.
  • 在所有负债组中表现出强大的打击检测能力 (平均丰富系数为13.1的5%).
  • 与现有的工具相比,ProfhEX模型在大规模的负债分析中显示出更高的预测能力.

结论:

  • ProfhEX提供了一个强大的AI驱动的解决方案,用于药物发现中的早期责任概况.
  • 该平台有效预测潜在的不良影响,有助于选择更安全的候选药物.
  • 未来的扩展包括新的目标和互补的建模方法,提高其实用性.