Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning
Abstract Predicting organic reaction feasibility and robustness against environmental factors is challenging. We address this issue by integrating high throughput experimentation (HTE) and Bayesian deep learning. Diverging from existing HTE studies focused on niche chemical spaces, in this work, our...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-59812-0 |
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| _version_ | 1849309753290784768 |
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| author | Haowen Zhong Yilan Liu Haibin Sun Yuru Liu Rentao Zhang Baochen Li Yi Yang Yuqing Huang Fei Yang Frankie S. Mak Klement Foo Sen Lin Tianshu Yu Peng Wang Xiaoxue Wang |
| author_facet | Haowen Zhong Yilan Liu Haibin Sun Yuru Liu Rentao Zhang Baochen Li Yi Yang Yuqing Huang Fei Yang Frankie S. Mak Klement Foo Sen Lin Tianshu Yu Peng Wang Xiaoxue Wang |
| author_sort | Haowen Zhong |
| collection | DOAJ |
| description | Abstract Predicting organic reaction feasibility and robustness against environmental factors is challenging. We address this issue by integrating high throughput experimentation (HTE) and Bayesian deep learning. Diverging from existing HTE studies focused on niche chemical spaces, in this work, our in-house HTE platform conducted 11,669 distinct acid amine coupling reactions in 156 working hours, yielding the most extensive single HTE dataset at a volumetric scale for industrial delivery. Our Bayesian neural network model achieved a benchmark for prediction accuracy of 89.48% for reaction feasibility. Furthermore, our fine-grained uncertainty disentanglement enables efficient active learning, reducing 80% of data requirements. Additionally, our uncertainty analysis effectively identifies out-of-domain reactions and evaluates reaction robustness or reproducibility against environmental factors for scaling up, offering a practical framework for navigating chemical spaces and designing highly robust industrial processes. |
| format | Article |
| id | doaj-art-bb707eeb374746f0b42bc918eb225644 |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-bb707eeb374746f0b42bc918eb2256442025-08-20T03:53:58ZengNature PortfolioNature Communications2041-17232025-05-0116111110.1038/s41467-025-59812-0Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learningHaowen Zhong0Yilan Liu1Haibin Sun2Yuru Liu3Rentao Zhang4Baochen Li5Yi Yang6Yuqing Huang7Fei Yang8Frankie S. Mak9Klement Foo10Sen Lin11Tianshu Yu12Peng Wang13Xiaoxue Wang14ChemLexChemLexChemLexChemLexChemLexChemLexChemLexMegaRobo Technologies Co., Ltd.Zhejiang LaboratoryExperimental Drug Development Centre (EDDC), Agency for Science, Technology and Research (A*STAR)Experimental Drug Development Centre (EDDC), Agency for Science, Technology and Research (A*STAR)ChemLexSchool of Data Science, The Chinese University of Hong Kong - ShenzhenChemLexChemLexAbstract Predicting organic reaction feasibility and robustness against environmental factors is challenging. We address this issue by integrating high throughput experimentation (HTE) and Bayesian deep learning. Diverging from existing HTE studies focused on niche chemical spaces, in this work, our in-house HTE platform conducted 11,669 distinct acid amine coupling reactions in 156 working hours, yielding the most extensive single HTE dataset at a volumetric scale for industrial delivery. Our Bayesian neural network model achieved a benchmark for prediction accuracy of 89.48% for reaction feasibility. Furthermore, our fine-grained uncertainty disentanglement enables efficient active learning, reducing 80% of data requirements. Additionally, our uncertainty analysis effectively identifies out-of-domain reactions and evaluates reaction robustness or reproducibility against environmental factors for scaling up, offering a practical framework for navigating chemical spaces and designing highly robust industrial processes.https://doi.org/10.1038/s41467-025-59812-0 |
| spellingShingle | Haowen Zhong Yilan Liu Haibin Sun Yuru Liu Rentao Zhang Baochen Li Yi Yang Yuqing Huang Fei Yang Frankie S. Mak Klement Foo Sen Lin Tianshu Yu Peng Wang Xiaoxue Wang Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning Nature Communications |
| title | Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning |
| title_full | Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning |
| title_fullStr | Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning |
| title_full_unstemmed | Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning |
| title_short | Towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning |
| title_sort | towards global reaction feasibility and robustness prediction with high throughput data and bayesian deep learning |
| url | https://doi.org/10.1038/s41467-025-59812-0 |
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