PDF(Pubmed)
Abstract：
The accurate prediction of adverse drug reactions (ADRs) is essential for comprehensive drug safety evaluation. Pre-trained deep chemical language models have emerged as powerful tools capable of automatically learning molecular structural features from large-scale datasets, showing promising capabilities for the downstream prediction of molecular properties. However, the performance of pre-trained chemical language models in predicting ADRs, especially idiosyncratic ADRs induced by marketed drugs, remains largely unexplored. In this study, we propose MoLFormer-XL, a pre-trained model for encoding molecular features from canonical SMILES, in conjunction with a CNN-based model to predict drug-induced QT interval prolongation (DIQT), drug-induced teratogenicity (DIT), and drug-induced rhabdomyolysis (DIR). Our results demonstrate that the proposed model outperforms conventional models applied in previous studies for predicting DIQT, DIT, and DIR. Notably, an analysis of the learned linear attention maps highlights amines, alcohol, ethers, and aromatic halogen compounds as strongly associated with the three types of ADRs. These findings hold promise for enhancing drug discovery pipelines and reducing the drug attrition rate due to safety concerns.
摘要：
药物不良反应(ADR)的准确预测是药物安全性综合评价的关键。预先训练的深度化学语言模型已经成为能够从大规模数据集中自动学习分子结构特征的强大工具。显示了对分子性质的下游预测的有希望的能力。然而,预先训练的化学语言模型在预测ADR中的表现，特别是由上市药物引起的特异性ADR，在很大程度上仍未探索。在这项研究中,我们提议MoLFormer-XL，用于从规范SMILES编码分子特征的预训练模型，结合基于CNN的模型来预测药物诱导的QT间期延长(DIQT)，药物诱导的致畸性（DIT），和药物诱导的横纹肌溶解症（DIR）。我们的结果表明，所提出的模型优于以前在预测DIQT的研究中应用的传统模型，DIT,和DIR。值得注意的是,对学习的线性注意力图的分析突出了胺，酒精,醚,和芳香卤素化合物与这三种类型的ADR密切相关。这些发现有望增强药物发现渠道并降低由于安全问题而导致的药物流失率。
