medical-10

Chemical Reaction Prediction

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in chemical-reaction-prediction-10

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Libraries

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Use these libraries to find chemical-reaction-prediction-10 models and implementations

Datasets

USPTO-50k

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Most implemented papers

Chemical-Reaction-Aware Molecule Representation Learning

Jiawei Han, Heng Ji, Kyunghyun Cho, Weijian Li, Hongwei Wang, Xiaomeng Jin, M. Burke•Mon Sep 20 2021

The key idea of the approach is to preserve the equivalence of molecules with respect to chemical reactions in the embedding space, i.e., forcing the sum of reactant and product embeddings to be equal for each chemical equation.

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Root-aligned SMILES: a tight representation for chemical reaction prediction

Jie Song, Mingli Song, Zipeng Zhong, Zunlei Feng, Tiantao Liu, Lingxiang Jia, Shaolun Yao, Min-Ying Wu, Tingjun Hou•Mon Mar 21 2022

This article proposes the root-aligned SMILES (R-SMILES), which is a tightly aligned one-to-one mapping between the product and the reactant SMilES for more e-cient synthesis prediction and shows that it signiﬁcantly outperforms them all, demonstrating the superiority of the proposed method.

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A generalized-template-based graph neural network for accurate organic reactivity prediction

Shuan Chen, Yousung Jung•Wed Aug 31 2022

A chemistry-motivated graph neural network called LocalTransform is proposed, which learns organic reactivity based on generalized reaction templates to describe the net changes in electron configuration between the reactants and products and exhibits state-of-the-art product prediction accuracy.

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Graph Neural Networks-based Hybrid Framework For Predicting Particle Crushing Strength

Min-Gyoo Song, Zunlei Feng, Tongya Zheng, Tianli Zhang, Qingzheng Guan, Wenjie Huang, Chun Chen•Tue Jul 25 2023

A hybrid framework based on GNNs is devised to predict particle crushing strength in a particle fragment view with the advances of state of the art GNNS and is compared against traditional machine learning methods and the plain MLP to verify its effectiveness.

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