论文标题
使用反应预测的分子性质预测的训练前变压器
Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction
论文作者
论文摘要
分子性质预测在化学中至关重要，尤其是对于药物发现应用。但是，可用的分子属性数据通常受到限制，鼓励信息从相关数据传输。转移学习对计算机视觉和自然语言处理信号等领域产生了巨大影响，以实现其在分子财产预测中的潜力。我们提出了使用反应数据进行分子表示学习的预训练程序，并使用它来预训练微笑变压器。我们对物理化学，生物物理学和生理学中的分子的12个分子性质预测任务进行微调和评估了预训练的模型，与非训练的基线模型相比，对12个任务中的5个任务中的5项表现出统计学上显着的积极作用。
Molecular property prediction is essential in chemistry, especially for drug discovery applications. However, available molecular property data is often limited, encouraging the transfer of information from related data. Transfer learning has had a tremendous impact in fields like Computer Vision and Natural Language Processing signaling for its potential in molecular property prediction. We present a pre-training procedure for molecular representation learning using reaction data and use it to pre-train a SMILES Transformer. We fine-tune and evaluate the pre-trained model on 12 molecular property prediction tasks from MoleculeNet within physical chemistry, biophysics, and physiology and show a statistically significant positive effect on 5 of the 12 tasks compared to a non-pre-trained baseline model.
