log-RRIM: Yield Prediction via Local-to-Global Reaction Representation Learning and Interaction Modeling. [PDF]
J Chem Inf ModelAccurate prediction of chemical reaction yields is crucial for optimizing organic synthesis, potentially reducing time and resources spent on experimentation. With the rise of artificial intelligence (AI), there is growing interest in leveraging AI-based
Hu X, Chen Z, Adu-Ampratwum D, Ning X.
europepmc +3 more sources
ACS OmegaThe chemical reaction yield is an important factor to determine the reaction conditions. Recently, many data-driven models for yield prediction using high-throughput experimentation datasets have been reported.
Akinori Sato +2 more
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BatGPT-Chem: A Foundation Large Model for Chemical Engineering [PDF]
ResearchLarge language models (LLMs) have showcased remarkable capabilities in the realm of AI for Science, and chemistry has greatly benefited from the advancement of AI tools.
Yifei Yang +7 more
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Reaction-conditioned generative model for catalyst design and optimization with CatDRX [PDF]
Communications ChemistryDesigning effective catalysts is a key process for optimizing catalytic reactions to reduce time and waste during scale-up. Recently proposed approaches, including generative models, show promise in identifying new catalysts.
Apakorn Kengkanna +3 more
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Prediction of Esterification and Antioxidant Properties of Food-Derived Fatty Acids and Ascorbic Acid Based on Machine Learning: A Review [PDF]
FoodsThis study is dedicated to summarizing and performing an in-depth analysis of the antioxidant properties of ascorbic acid fatty acid esters. The esterification reaction mechanism of ascorbic acid with palmitic acid, lauric acid, and oleic acid in food ...
Xinyu Wang +5 more
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Interpretation of chemical reaction yields with graph neural additive network
Machine Learning: Science and TechnologyPrediction of chemical yields is crucial for exploring untapped chemical reactions and optimizing synthetic pathways for targeted compounds. Recently, graph neural networks have proven successful in achieving high predictive accuracy.
Youngchun Kwon +3 more
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Journal of CheminformaticsThis paper presents AutoTemplate, an innovative data preprocessing protocol, addressing the crucial need for high-quality chemical reaction datasets in the realm of machine learning applications in organic chemistry.
Lung-Yi Chen, Yi-Pei Li
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Multimodal Transformer-based Model for Buchwald-Hartwig and Suzuki-Miyaura Reaction Yield Prediction [PDF]
arXiv.org, 2022 Predicting the yield percentage of a chemical reaction is useful in many aspects such as reducing wet-lab experimentation by giving the priority to the reactions with a high predicted yield. In this work we investigated the use of multiple type inputs to
Shimaa Baraka, A. E. Kerdawy
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MetaRF: Differentiable Random Forest for Reaction Yield Prediction with a Few Trails [PDF]
arXiv.org, 2022 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 ...
Ke Chen +4 more
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Machine Learning C–N Couplings: Obstacles for a General-Purpose Reaction Yield Prediction
ACS Omega, 2023 Pd-catalyzed C–N couplings are commonplace in academia and industry. Despite their significance, finding suitable reaction conditions leading to a high yield, for instance, remains a challenging and time-consuming task which usually requires screening ...
Martin Fitzner +4 more
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