机器学习技术被广泛用于药物发现,重点是开发解释潜在药物结构信息的QSAR模型。在这项研究中,预先训练的自然语言处理(NLP)模型,ChemBERTa,被用于药物发现过程。我们提出并评估了四个核心模型架构:深度神经网络(DNN),编码器,串联(concat),和管道。DNN模型处理物理化学性质作为输入,而编码器模型利用简化的分子输入线进入系统(SMILES)以及NLP技术。后两种模式,concat和管道,结合了SMILES和物理化学性质,以并行和顺序的方式运行,分别。我们从DrugBank收集了5238条条目,包括它们的物理化学性质和吸收,分布,新陈代谢,排泄,和毒性(ADMET)特征。模型性能通过接收器工作特征曲线下面积(AUROC)评估,DNN,编码器,concat,管道型号达到62.4%,76.0%,74.9%,和68.2%,分别。在84个实验微粒体稳定性数据集的单独测试中,外部数据的AUROC分数为DNN的78%,44%的编码器,和50%的Concat,表明DNN模型对新数据具有优越的预测能力。这表明基于结构信息的模型可能需要进一步优化或替代标记化策略。自然语言处理技术在制药挑战中的应用已经证明了有希望的结果,强调需要更广泛的数据来增强模型泛化。
Machine learning techniques are extensively employed in drug discovery, with a significant focus on developing QSAR models that interpret the structural information of potential drugs. In this study, the pre-trained natural language processing (NLP) model, ChemBERTa, was utilized in the drug discovery process. We proposed and evaluated four core model architectures as follows: deep neural network (DNN), encoder, concatenation (concat), and pipe. The DNN model processes physicochemical properties as input, while the encoder model leverages the simplified molecular input line entry system (SMILES) along with NLP techniques. The latter two models, concat and pipe, incorporate both SMILES and physicochemical properties, operating in parallel and with sequential manners, respectively. We collected 5238 entries from DrugBank, including their physicochemical properties and absorption, distribution, metabolism, excretion, and toxicity (ADMET) features. The models\' performance was assessed by the area under the receiver operating characteristic curve (AUROC), with the DNN, encoder, concat, and pipe models achieved 62.4%, 76.0%, 74.9%, and 68.2%, respectively. In a separate test with 84 experimental microsomal stability datasets, the AUROC scores for external data were 78% for DNN, 44% for the encoder, and 50% for concat, indicating that the DNN model had superior predictive capabilities for new data. This suggests that models based on structural information may require further optimization or alternative tokenization strategies. The application of natural language processing techniques to pharmaceutical challenges has demonstrated promising results, highlighting the need for more extensive data to enhance model generalization.