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一个基于BERT预训练模型的深度学习模型,用于预测抗癌化学化合物的抗增殖活性

M Torabi¹, I Haririan^2,3, A Foroumadi^4,5

¹Biosensor Research Centre, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

SAR and QSAR in environmental research

|November 28, 2024

概括

像ChemBERTa这样的深度转移学习模型在预测抗癌药物活性方面表现有前途. 这项研究证明了它们在加速药物发现的潜力,通过准确地对各种癌症细胞系的分子有效性进行分类.

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
生物信息学是一种生物信息学.

背景情况:

药物发现旨在找到具有最小副作用的有效化合物.
传统的定量结构-活动关系 (QSAR) 研究由于高成本,时间和数据稀缺而面临限制.
深度转移学习模型为药物发现中的预测建模提供了一个有希望的替代方案.

研究的目的:

评估深度转移学习模型 (ChemBERTa) 在预测小分子对五种癌症细胞系的抗增殖活性方面的性能.
在大型公共数据集 (PubChem) 和较小的内部数据集上评估模型的准确性.

主要方法:

使用基于BERT的深度转移学习模型 (ChemBERTa) 进行预测分析.
在PubChem的3000多个合成分子上训练并验证了该模型,以预测抗增殖活性.
在使用IC50值的内部数据集上对该模型进行了测试,每个细胞系约有25个小分子,使用IC50值.

主要成果:

在PubChem数据集上,ChemBERTa模型在预测大多数癌症细胞系 (HeLa,MCF7,MDA-MB231) 的活性类别方面取得了可接受的准确性.
在最初的数据集中,PC3和MDA-MB细胞系的性能不那么可靠.
在内部数据集上,该模型显示HeLa的高精度和MCF7和MDA-MB231的可接受性能,但PC3和HepG2.2的可靠性较低.

结论:

微调的ChemBERTa模型展示了其作为预测药物疗效的有价值工具的潜力.
该模型对药物发现的应用有希望,特别是对预测新型内部数据集的结果.
可能需要进一步细化,以提高某些细胞系的预测可靠性,如PC3和HepG2.

关键词:

在BERT模型中,BERT模型是:癌症治疗治疗癌症治疗它们具有抗增殖活动的抗增殖活性.深度学习是一种深度学习.公司内部数据集

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