首页|Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center

Generic Interpretable Reaction Condition Predictions with Open Reaction Condition Datasets and Unsupervised Learning of Reaction Center

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Effective synthesis planning powered by deep learning(DL)can significantly accelerate the discovery of new drugs and materials.However,most DL-assisted synthesis planning methods offer either none or very limited capability to recommend suitable reaction conditions(RCs)for their reaction predictions.Currently,the prediction of RCs with a DL framework is hindered by several factors,including:(a)lack of a standardized dataset for benchmarking,(b)lack of a general prediction model with powerful representation,and(c)lack of interpretability.To address these issues,we first created 2 standardized RC datasets covering a broad range of reaction classes and then proposed a powerful and interpretable Transformer-based RC predictor named Parrot.Through careful design of the model architecture,pretraining method,and training strategy,Parrot improved the overall top-3 prediction accuracy on catalysis,solvents,and other reagents by as much as 13.44％,compared to the best previous model on a newly curated dataset.Additionally,the mean absolute error of the predicted temperatures was reduced by about 4 ℃.Furthermore,Parrot manifests strong generalization capacity with superior cross-chemical-space prediction accuracy.Attention analysis indicates that Parrot effectively captures crucial chemical information and exhibits a high level of interpretability in the prediction of RCs.The proposed model Parrot exemplifies how modern neural network architecture when appropriately pretrained can be versatile in making reliable,generalizable,and interpretable recommendation for RCs even when the underlying training dataset may still be limited in diversity.

Xiaorui Wang、Chang-Yu Hsieh、Xiaodan Yin、Jike Wang、Yuquan Li、Yafeng Deng、Dejun Jiang、Zhenxing Wu、Hongyan Du、Hongming Chen、Yun Li、Huanxiang Liu、Yuwei Wang、Pei Luo、Tingjun Hou、Xiaojun Yao

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Neher's Biophysics Laboratory for Innovative Drug Discovery,State Key Laboratory of Quality Research in Chinese Medicine,Macau Institute for Applied Research in Medicine and Health,Macau University of Science and Technology,Macao,999078,China

CarbonSilicon Al Technology Co.,Ltd,Hangzhou,Zhejiang 310018,China

Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University,College of Pharmaceutical Sciences,Zhejiang University,Hangzhou,310058,China

College of Chemistry and Chemical Engineering,Lanzhou University,Lanzhou,730000,China

Center of Chemistry and Chemical Biology,Guangzhou Regenerative Medicine and Health Guangdong Laboratory,Guangzhou 510530,China

Faculty of Applied Sciences,Macao Polytechnic University,Macao,999078,China

College of Pharmacy,Shaanxi University of Chinese Medicine,Xianyang,Shaanxi,712044,China

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Science and Technology Development Fund,Macau SARScience and Technology Development Fund,Macau SARScience and Technology Development Fund,Macau SARDr.Neher's Biophysics Laboratory for Innovative Drug Discovery,State Key Laboratory of Quality Research in Chinese Medicine,Maca

File 0056/2020/AMJ0114/2020/A30015/2019/AM J001/2020/ALC

2024

10.34133/research.0231

研究(英文)

研究(英文)

CSTPCD
ISSN：
年,卷(期)：2024.2024(2)
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