Machine learning is becoming a preferred method for the virtual screening of organic materials due to its cost-effectiveness over traditional computationally demanding techniques. However, the scarcity of labeled data for organic materials poses a significant challenge for training advanced machine learning models. This study showcases the potential of utilizing databases of drug-like small molecules and chemical reactions to pretrain the BERT model, enhancing its performance in the virtual screening of organic materials. By fine-tuning the BERT models with data from five virtual screening tasks, the version pretrained with the USPTO-SMILES dataset achieved R2 scores exceeding 0.94 for three tasks and over 0.81 for two others. This performance surpasses that of models pretrained on the small molecule or organic materials databases and outperforms three traditional machine learning models trained directly on virtual screening data. The success of the USPTO-SMILES pretrained BERT model can be attributed to the diverse array of organic building blocks in the USPTO database, offering a broader exploration of the chemical space. The study further suggests that accessing a reaction database with a wider range of reactions than the USPTO could further enhance model performance. Overall, this research validates the feasibility of applying transfer learning across different chemical domains for the efficient virtual screening of organic materials.

翻译：机器学习凭借其较传统高计算需求方法更具成本效益的优势，正逐渐成为有机材料虚拟筛选的首选方法。然而，有机材料标注数据的稀缺性对训练先进机器学习模型构成了重大挑战。本研究展示了利用类药小分子与化学反应数据库预训练BERT模型的潜力，以提升其在有机材料虚拟筛选中的性能。通过使用五项虚拟筛选任务的数据对BERT模型进行微调，采用USPTO-SMILES数据集预训练的版本在三个任务中获得了超过0.94的R²分数，在另外两个任务中超过0.81。该性能超越了基于小分子或有机材料数据库预训练的模型，并优于三种直接使用虚拟筛选数据训练的经典机器学习模型。USPTO-SMILES预训练BERT模型成功的原因可归因于USPTO数据库中多样化的有机构建单元，提供了更广泛的化学空间探索。研究进一步表明，接入比USPTO涵盖更多反应类型的反应数据库可进一步提升模型性能。总体而言，本研究验证了跨化学领域应用迁移学习实现有机材料高效虚拟筛选的可行性。