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Using machine learning (ML) techniques to predict material properties is a crucial research topic.
The properties of known drugs. 1. molecular frameworks
Guy W Bemis and Mark A Murcko · 1996
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Reoptimization of mdl keys for use in drug discovery
Joseph L Durant, Burton A Leland, Douglas R Henry, and James G Nourse · 2002
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Esol: estimating aqueous solubility directly from molecular structure
John S Delaney · 2004
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang · 2009
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Introducing the knowledge graph: things, not strings
Amit Singhal · 2012
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Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
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Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling
Greg Landrum et al · 2013
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Freesolv: a database of experimental and calculated hydration free energies, with input files
David L Mobley and J Peter Guthrie · 2014
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Big data of materials science: critical role of the descriptor
Luca M Ghiringhelli, Jan Vybiral, Sergey V Levchenko, Claudia Draxl, and Matthias Scheffler · 2015
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Knowledge graph embedding with numeric attributes of entities
Yanrong Wu and Zhichun Wang · 2018
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Embedding multimodal relational data for knowledge base completion
Pouya Pezeshkpour, Liyan Chen, and Sameer Singh · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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Data-driven materials science: status, challenges, and perspectives
Lauri Himanen, Amber Geurts, Adam Stuart Foster, and Patrick Rinke · 2019
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Machine learning in materials science
Jing Wei, Xuan Chu, Xiang-Yu Sun, Kun Xu, Hui-Xiong Deng, Jigen Chen, Zhongming Wei, and Ming Lei · 2019
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Using deep neural network with small dataset to predict material defects
Shuo Feng, Huiyu Zhou, and Hongbiao Dong · 2019
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Incorporating literals into knowledge graph embeddings
Agustinus Kristiadi, Mohammad Asif Khan, Denis Lukovnikov, Jens Lehmann, and Asja Fischer · 2019
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High-entropy alloys
Easo P George, Dierk Raabe, and Robert O Ritchie · 2019
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Machine learning assisted design of high entropy alloys with desired property
Cheng Wen, Yan Zhang, Changxin Wang, Dezhen Xue, Yang Bai, Stoichko Antonov, Lanhong Dai, Turab Lookman, and Yanjing Su · 2019
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Prediction of the composition and hardness of high-entropy alloys by machine learning
Yao-Jen Chang, Chia-Yung Jui, Wen-Jay Lee, and An-Chou Yeh · 2019
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Are learned molecular representations ready for prime time?
Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, et al · 2019
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Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism
Zhaoping Xiong, Dingyan Wang, Xiaohong Liu, Feisheng Zhong, Xiaozhe Wan, Xutong Li, Zhaojun Li, Xiaomin Luo, Kaixian Chen, Hualiang Jiang, et al · 2019
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N-gram graph: Simple unsupervised representation for graphs, with applications to molecules
Shengchao Liu, Mehmet F Demirel, and Yingyu Liang · 2019
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Compressive strength prediction of basalt fiber reinforced concrete via random forest algorithm
Hong Li, Jiajian Lin, Xiaobao Lei, and Tianxia Wei · 2022
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Molecular contrastive learning with chemical element knowledge graph
Yin Fang, Qiang Zhang, Haihong Yang, Xiang Zhuang, Shumin Deng, Wen Zhang, Ming Qin, Zhuo Chen, Xiaohui Fan, and Huajun Chen · 2022
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Material machine learning for alloys: Applications, challenges and perspectives
Xiujuan Liu, Pengcheng Xu, Juanjuan Zhao, Wencong Lu, Minjie Li, and Gang Wang · 2022
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Composition design of high-entropy alloys with deep sets learning
Jie Zhang, Chen Cai, George Kim, Yusu Wang, and Wei Chen · 2022
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From knowledge graph development to serving industrial knowledge automation: A review
Guangxuan Song, Dongmei Fu, and Dawei Zhang · 2022
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Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
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Deep Learning for the Life Sciences
Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande, Karl Leswing, and Zhenqin Wu · 2019
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Predicting thermal properties of crystals using machine learning
Sherif Abdulkader Tawfik, Olexandr Isayev, Michelle JS Spencer, and David A Winkler · 2020
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Examining hybrid and single svm models with different kernels to predict rock brittleness
Danial Jahed Armaghani, Panagiotis G Asteris, Behnam Askarian, Mahdi Hasanipanah, Reza Tarinejad, and Van Van Huynh · 2020
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Learning triple embeddings from knowledge graphs
Valeria Fionda and Giuseppe Pirrò · 2020
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Latte: Latent type modeling for biomedical entity linking
Ming Zhu, Busra Celikkaya, Parminder Bhatia, and Chandan K Reddy · 2020
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A strong and ductile medium-entropy alloy resists hydrogen embrittlement and corrosion
Hong Luo, Seok Su Sohn, Wenjun Lu, Linlin Li, Xiaogang Li, Chandrahaasan K Soundararajan, Waldemar Krieger, Zhiming Li, and Dierk Raabe · 2020
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Communicative representation learning on attributed molecular graphs
Ying Song, Shuangjia Zheng, Zhangming Niu, Zhang-Hua Fu, Yutong Lu, and Yuedong Yang · 2020
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How does knowledge graph embedding extrapolate to unseen data: a semantic evidence view
Ren Li, Yanan Cao, Qiannan Zhu, Guanqun Bi, Fang Fang, Yi Liu, and Qian Li · 2022
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Cohesive clustering algorithm based on high-dimensional generalized fermat points
Tong Li, Xiujuan Wang, and Hao Zhong · 2022
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A machine learning-based alloy design system to facilitate the rational design of high entropy alloys with enhanced hardness
Chen Yang, Chang Ren, Yuefei Jia, Gang Wang, Minjie Li, and Wencong Lu · 2022
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gpt-3.5 model
OpenAI · 2022
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Geometry-enhanced molecular representation learning for property prediction
Xiaomin Fang, Lihang Liu, Jieqiong Lei, Donglong He, Shanzhuo Zhang, Jingbo Zhou, Fan Wang, Hua Wu, and Haifeng Wang · 2022
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Prediction of phase and hardness of heas based on constituent elements using machine learning models
Mahmoud Bakr, Junaidi Syarif, and Ibrahim Abaker Targio Hashem · 2022
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Molecular contrastive learning of representations via graph neural networks
Yuyang Wang, Jianren Wang, Zhonglin Cao, and Amir Barati Farimani · 2022
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langchain
LangChain · 2022
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Openai embeddings
OpenAI · 2022
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Data-driven corrosion inhibition efficiency prediction model incorporating 2d-3d molecular graphs and inhibitor concentration
Jinbo Ma, Jiaxin Dai, Xin Guo, Dongmei Fu, Lingwei Ma, Patrick Keil, Arjan Mol, and Dawei Zhang · 2023
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Knowledge graph-enhanced molecular contrastive learning with functional prompt
Yin Fang, Qiang Zhang, Ningyu Zhang, Zhuo Chen, Xiang Zhuang, Xin Shao, Xiaohui Fan, and Huajun Chen · 2023
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Relational data embeddings for feature enrichment with background information
Alexis Cvetkov-Iliev, Alexandre Allauzen, and Gaël Varoquaux · 2023
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Uni-mol: A universal 3d molecular representation learning framework
Gengmo Zhou, Zhifeng Gao, Qiankun Ding, Hang Zheng, Hongteng Xu, Zhewei Wei, Linfeng Zhang, and Guolin Ke · 2023
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Srr-ddi: A drug–drug interaction prediction model with substructure refined representation learning based on self-attention mechanism
Dongjiang Niu, Lei Xu, Shourun Pan, Leiming Xia, and Zhen Li · 2024
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Identifying the reaction centers of molecule based on dual-view representation
Hui Yu, Jing Wang, Chao Song, and Jian-Yu Shi · 2024
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