Machine learning prediction of organic materials properties is an efficient
virtual screening method ahead of more expensive screening methods. However,
this approach has suffered from insufficient labeled data on organic materials
to train state-of-the-art machine learning models. In this study, we
demonstrate that drug-like small molecule and chemical reaction databases can
be used to pretrain the BERT model for the virtual screening of organic
materials. Among the BERT models fine-tuned by five virtual screening tasks on
organic materials, the USPTO-SMILES pretrained BERT model had R2 > 0.90 for two
tasks and R2 > 0.82 for one, which was generally superior to the same models
pretrained by the small molecule or organic materials databases, as well as to
the other three traditional machine learning models trained directly on the
virtual screening task data. The superior performance of the USPTO-SMILES
pretrained BERT model is due to the greater variety of organic building blocks
in the USPTO database and the broader coverage of the chemical space. The even
better performance of the BERT model pretrained externally from a chemical
reaction database with additional sources of chemical reactions strengthens our
proof of concept that transfer learning across different chemical domains is
practical for the virtual screening of organic materials.

使用机器学习预测有机材料属性是一种高效的虚拟筛选方法，本研究证明药物类小分子和化学反应数据库可用于预训练 BERT 模型以进行有机材料的虚拟筛选，并展示了其在机器学习模型训练中的优越性能，进一步加强了跨不同化学领域的迁移学习在有机材料的虚拟筛选中的可行性。

跨不同化学领域的迁移学习：利用在小分子和化学反应数据上预训练的深度学习模型进行有机材料的虚拟筛选

Transfer Learning across Different Chemical Domains: Virtual Screening  of Organic Materials with Deep Learning Models Pretrained on Small Molecule  and Chemical Reaction Data

Automatic design of organic materials requires black-box optimization in a
vast chemical space. In conventional molecular design algorithms, a molecule is
built as a combination of predetermined fragments. Recently, deep neural
network models such as variational auto encoders (VAEs) and recurrent neural
networks (RNNs) are shown to be effective in de novo design of molecules
without any predetermined fragments. This paper presents a novel python library
ChemTS that explores the chemical space by combining Monte Carlo tree search
(MCTS) and an RNN. In a benchmarking problem of optimizing the octanol-water
partition coefficient and synthesizability, our algorithm showed superior
efficiency in finding high-scoring molecules. ChemTS is available at
this https URL

该研究论文提出了一种新颖的 Python 库 ChemTS，该库通过结合 Monte Carlo Tree Search（MCTS）和递归神经网络（RNN）依据无预定化合物片段探索化学空间，实现有机材料的自动设计并获得了优于传统方法的高效率和准确度。