Results 241 to 250 of about 335,422 (272)

Improving Reaction Yield Prediction with Chemical Atom-Level Reaction Learning

Journal of Chemical Information and Modeling
Reaction yield, the percentage of reactants successfully converted into desired products relative to the theoretical maximum, is a critical metric for evaluating chemical reaction efficiency.
Yijingxiu Lu, Yinhua Piao, Ming Shen, Sun Kim   +3 more
semanticscholar   +1 more source

Exploring BERT for Reaction Yield Prediction: Evaluating the Impact of Tokenization, Molecular Representation, and Pretraining Data Augmentation

Journal of Chemical Information and Modeling
Predicting reaction yields in synthetic chemistry remains a significant challenge. This study systematically evaluates the impact of tokenization, molecular representation, pretraining data, and adversarial training on a BERT-based model for yield ...
Adrian Krzyzanowski, Stephen D. Pickett, Peter Pogány   +2 more
semanticscholar   +1 more source

An active representation learning method for reaction yield prediction with small-scale data

Communications Chemistry
Reaction optimization plays an essential role in chemical research and industrial production. To explore a large reaction system, a practical issue is how to reduce the heavy experimental load for finding the high-yield conditions.
Pengxiang Hua, Zhen Huang, Zhe-Yuan Xu, Qiang Zhao, Chen-Yang Ye, Yi-Feng Wang, Yun‐He Xu, Yao Fu, Hu Ding   +8 more
semanticscholar   +1 more source

Reaction classification and yield prediction using the differential reaction fingerprint DRFP

Digital Discovery
In Reaction classification and yield prediction using the differential reaction fingerprint DRFP, we introduced a chemical reaction fingerprint based on the symmetric difference A△B of two sets A and B....
Daniel Probst
semanticscholar   +1 more source

Multi‐modal Homogeneous Chemical Reaction Performance Prediction with Graph and Chemical Language Information

Chinese journal of chemistry
Accurate prediction for chemical reaction performance offers optimal direction for synthetic development. To this end, we present a novel multi‐modal model called MMHRP‐GCL to achieve the prediction of homogeneous chemical reaction yield ...
Shen Wang, Weiren Zhao, Yining Liu, Yang Li   +3 more
semanticscholar   +1 more source

Proto-Yield: An Uncertainty-Aware Prototype Network for Yield Prediction in Real-world Chemical Reactions

International Conference on Information and Knowledge Management
Reaction yield prediction underpins computer-aided synthesis prediction (CASP). Formulated as a regression problem that takes both reactants and products as input, this task has been extensively studied using machine learning methods, based on ...
Kehan Guo, Zhen Liu, Zhichun Guo, B. Nan, O. Isayev, Nitesh V. Chawla, Olaf Wiest, Xiangliang Zhang   +7 more
semanticscholar   +1 more source

Designing Buchwald-Hartwig Reaction Graph for Yield Prediction.

Journal of Organic Chemistry
The Buchwald-Hartwig (B-H) reaction graph, a novel graph for deep learning models, is designed to simulate the interactions among multiple chemical components in the B-H reaction by representing each reactant as an individual node within a custom ...
Weiren Zhao, Shen Wang, Yang Li
semanticscholar   +1 more source

Chemical Reaction Prediction using Machine Learning

Research Journal of Pharmacy and Technology
A significant revolution in organic chemistry is being driven by artificial intelligence. A number of platforms, including applications for planned synthesis and reaction prediction Machine learning has successfully integrated itself into the daily work ...
Adnan R. Ahmad
semanticscholar   +1 more source

Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space.

Chemical Communications, 2019
Predicting how a complex molecule reacts with different reagents, and how to synthesise complex molecules from simpler starting materials, are fundamental to organic chemistry.
A. Lee, Qingyi Yang, Vishnu Sresht, P. Bolgar, Xinjun Hou, Jacquelyn L. Klug-McLeod, C. Butler   +6 more
semanticscholar   +1 more source

Lifelong Machine Learning Potentials for Chemical Reaction Network Explorations

Journal of Chemical Theory and Computation
Recent developments in computational chemistry facilitate the automated quantum chemical exploration of chemical reaction networks for the in-silico prediction of synthesis pathways, yield, and selectivity. However, the underlying quantum chemical energy
Marco Eckhoff, Markus Reiher
semanticscholar   +1 more source