Results 1 to 10 of about 335,323 (173)

Improving chemical reaction yield prediction using pre-trained graph neural networks [PDF]

Journal of Cheminformatics
Graph neural networks (GNNs) have proven to be effective in the prediction of chemical reaction yields. However, their performance tends to deteriorate when they are trained using an insufficient training dataset in terms of quantity or diversity.
Jongmin Han, Youngchun Kwon, Youn-Suk Choi, Seokho Kang   +3 more
doaj   +3 more sources

ReactionT5: a pre-trained transformer model for accurate chemical reaction prediction with limited data [PDF]

Journal of Cheminformatics
Accurate chemical reaction prediction is critical for reducing both cost and time in drug development. This study introduces ReactionT5, a transformer-based chemical reaction foundation model pre-trained on the Open Reaction Database—a large publicly ...
Tatsuya Sagawa, Ryosuke Kojima
doaj   +3 more sources

Prediction of Synthesis Yield of Polymethoxy Dibutyl Ether Under Small Sample Conditions [PDF]

Molecules
In chemical reaction processes, yield prediction frequently faces challenges, such as multi-variable coupling, significant nonlinearity, and the limited accuracy of traditional mechanistic models.
Xue Wang, Linyu Lu, Qiuxin Ma, Hongyan Shang, Lanyi Sun   +4 more
doaj   +3 more sources

Application of the digital annealer unit in optimizing chemical reaction conditions for enhanced production yields [PDF]

Journal of Cheminformatics
Finding optimal reaction conditions is crucial for chemical synthesis in the pharmaceutical and chemical industries. However, due to the vast chemical space, conducting experiments for all the possible combinations is impractical.
Shih-Cheng Li, Pei-Hua Wang, Jheng-Wei Su, Wei-Yin Chiang, Tzu-Lan Yeh, Alex Zhavoronkov, Shih-Hsien Huang, Yen-Chu Lin, Chia-Ho Ou, Chih-Yu Chen   +9 more
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When SMILES Smiles, Practicality Judgment and Yield Prediction of Chemical Reaction via Deep Chemical Language Processing

IEEE Access, 2021
Simplified Molecular Input Line Entry System (SMILES) provides a text-based encoding method to describe the structure of chemical species and formulize general chemical reactions.
Shu Jiang, Zhuosheng Zhang, Hai Zhao, Jiangtong Li, Yang Yang, Bao-Liang Lu, Ning Xia   +6 more
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Enhancing Generic Reaction Yield Prediction through Reaction Condition-Based Contrastive Learning

Research, 2023
Deep learning (DL)-driven efficient synthesis planning may profoundly transform the paradigm for designing novel pharmaceuticals and materials. However, the progress of many DL-assisted synthesis planning (DASP) algorithms has suffered from the lack of ...
Xiaodan Yin, Chang-Yu Hsieh, Xiaorui Wang, Zhenxing Wu, Qing Ye, Honglei Bao, Yafeng Deng, Hongming Chen, Pei Luo, Huanxiang Liu, Tingjun Hou, Xiaojun Yao   +11 more
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MetaRF: attention-based random forest for reaction yield prediction with a few trails

Journal of Cheminformatics, 2023
Artificial intelligence has deeply revolutionized the field of medicinal chemistry with many impressive applications, but the success of these applications requires a massive amount of training samples with high-quality annotations, which seriously ...
Kexin Chen, Guangyong Chen, Junyou Li, Yuansheng Huang, Ercheng Wang, Tingjun Hou, Pheng-Ann Heng   +6 more
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Learning Hierarchical Representations for Explainable Chemical Reaction Prediction

Applied Sciences, 2023
This paper aims to propose an explainable and generalized chemical reaction representation method for accelerating the evaluation of the chemical processes in production. To this end, we designed an explainable coarse-fine level representation model that
Jingyi Hou, Zhen Dong
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Uncertainty-aware prediction of chemical reaction yields with graph neural networks

Journal of Cheminformatics, 2022
In this paper, we present a data-driven method for the uncertainty-aware prediction of chemical reaction yields. The reactants and products in a chemical reaction are represented as a set of molecular graphs.
Youngchun Kwon, Dongseon Lee, Youn-Suk Choi, Seokho Kang   +3 more
doaj   +2 more sources

Reaction performance prediction with an extrapolative and interpretable graph model based on chemical knowledge

Nature Communications, 2023
Accurate prediction of reactivity and selectivity provides the desired guideline for synthetic development. Due to the high-dimensional relationship between molecular structure and synthetic function, it is challenging to achieve the predictive modelling
Shu-Wen Li, Li-Cheng Xu, Cheng Zhang, Shuo-Qing Zhang, Xin Hong   +4 more
doaj   +2 more sources