School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China; Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, Gansu, China.
School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu, China.
J Mol Graph Model. 2023 Jun;121:108454. doi: 10.1016/j.jmgm.2023.108454. Epub 2023 Mar 15.
Simplified Molecular-Input Line-Entry System (SMILES) is one of a widely used molecular representation methods for molecular property prediction. We conjecture that all the characters in the SMILES string of a molecule are essential for making up the molecules, but most of them make little contribution to determining a particular property of the molecule. Therefore, we verified the conjecture in the pre-experiment. Motivated by the result, we propose to inject proper noisy information into the SMILES to augment the training data by increasing the diversity of the labeled molecules. To this end, we explore injecting perturbing noise into the original labeled SMILES strings to construct augmented data for alleviating the limitation of the labeled compound data and enhancing the model to extract more useful molecular representation for molecular property prediction. Specifically, we directly adopt mask, swap, deletion, and fusion operations on SMILES strings to randomly mask, swap, and delete atoms in SMILES strings. Then, the augmented data is used by two strategies: each epoch alternately feeds the original and perturbing noisy molecules, or each batch alternately feeds the original and perturbing noisy molecules. We conduct experiments on both Transformer and BiGRU models to validate the effectiveness by adopting widely used datasets from MoleculeNet and ZINC. Experimental results demonstrate that the proposed method outperforms strong baselines on all the datasets. NoiseMol obtains the best performance on BBBP and FDA when compared with state-of-the-art methods. Besides, NoiseMol achieves the best accuracy on LogP. Therefore, injecting perturbing noise into the labeled SMILES strings is an effective and efficient method, which improves the prediction performance, generalization, and robustness of the deep learning models.
SMILES（简化分子线性输入规范）是一种广泛用于分子性质预测的分子表示方法。我们推测分子的 SMILES 字符串中的所有字符对于构成分子都是必不可少的，但它们大多数对确定分子的特定性质贡献不大。因此，我们在预实验中验证了这一假设。受结果的启发，我们提出向 SMILES 中注入适当的噪声信息，通过增加标记分子的多样性来扩充训练数据。为此，我们探索了向原始标记 SMILES 字符串中注入干扰噪声，以构建扩充数据，从而缓解标记化合物数据的局限性，并增强模型以提取更多有用的分子表示，用于分子性质预测。具体来说，我们直接在 SMILES 字符串上采用掩码、交换、删除和融合操作，随机屏蔽、交换和删除 SMILES 字符串中的原子。然后，使用两种策略处理扩充数据：每个时期交替输入原始分子和干扰噪声分子，或者每个批次交替输入原始分子和干扰噪声分子。我们在 Transformer 和 BiGRU 模型上进行了实验，通过采用来自 MoleculeNet 和 ZINC 的广泛使用的数据集来验证其有效性。实验结果表明，所提出的方法在所有数据集上都优于强大的基线。与最先进的方法相比，NoiseMol 在 BBBP 和 FDA 上的性能最佳。此外，NoiseMol 在 LogP 上的准确率最高。因此，向标记的 SMILES 字符串中注入干扰噪声是一种有效且高效的方法，可提高深度学习模型的预测性能、泛化能力和鲁棒性。
