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在药物发现中用于ADMET预测的混合碎片-SMILES代币化

Nicholas Aksamit¹, Alain Tchagang², Yifeng Li^3,4

¹Department of Computer Science, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, ON, L2S 3A1, Canada.

BMC bioinformatics

|August 1, 2024

概括

这项研究引入了一种新的混合SMILES片段标记化方法,以改善药物发现. 这种人工智能方法增强了对候选药物的吸收,分布,新陈代谢,分泌和毒性 (ADMET) 属性的预测.

科学领域:

计算化学的计算化学
人工智能在药物发现中的作用
机器学习用于化学信息学

背景情况:

药物发现是一个昂贵和耗时的过程,需要分子满足多个吸收,分布,新陈代谢,分泌和毒性 (ADMET) 标准.
人工智能 (AI) 在药物发现和开发方面提供了潜在的改进.
以信息形式表示分子对于优化in-silicoAI解决方案至关重要.

研究的目的:

为基于变压器的模型引入和评估一种新的混合SMILES片段代币化方法.
调查这种混合代币化对ADMET预测任务的性能的影响.
将混合方法与标准SMILES代币化进行比较.

主要方法:

使用基于变压器的模型,特别是MTL-BERT,仅用于编码器的架构.
开发了一种混合代币化策略,将SMILES字符串与分子碎片结合起来.
调查了各种碎片库的切断,以评估混合方法的有效性.

主要成果:

混合SMILES片段代币化方法,特别是高频片段,与标准SMILES代币化相比,改善了ADMET预测性能.
发现过多的碎片可能会阻碍模型的性能.
该研究证明了整合碎片和字符级分子特征的有效性.

结论:

结合分子碎片的混合代币化增强了用于ADMET属性预测的变压器模型性能.
优化碎片包含平衡是最大限度地提高预测准确性的关键.
这种方法代表了人工智能驱动的药物发现分子表示的重大进步.

关键词:

在ADMET预测预测.药物发现药物发现碎片碎片碎片斯米莱斯 (SMILES) 是一个有趣的小孩.变压器变压器变压器

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