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Molecular Relational Learning (MRL), aiming to understand interactions between molecular pairs, plays a pivotal role in advancing biochemical research.
Language models are few-shot learners
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Scibert: A pretrained language model for scientific text
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Drug-drug adverse effect prediction with graph co-attention
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Strategies for pre-training graph neural networks
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Gognn: Graph of graphs neural network for predicting structured entity interactions
Hanchen Wang, Defu Lian, Ying Zhang, Lu Qin, and Xuemin Lin. 2020 · 2005
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Mathematical correlations for describing solute transfer into functionalized alkane solvents containing hydroxyl, ether, ester or ketone solvents
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Minnesota solvation database (mnsol) version 2012
Aleksandr V Marenich, Casey P Kelly, Jason D Thompson, Gregory D Hawkins, Candee C Chambers, David J Giesen, Paul Winget, Christopher J Cramer, and Donald G Truhlar. 2020 · 2012
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Data-driven prediction of drug effects and interactions
Nicholas P Tatonetti, Patrick P Ye, Roxana Daneshjou, and Russ B Altman. 2012 · 2012
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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 · 2014
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Zinc 15–ligand discovery for everyone
Teague Sterling and John J Irwin. 2015 · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling. 2017 · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2017 · 2017
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Estimation of solvation quantities from experimental thermodynamic data: Development of the comprehensive compsol databank for pure and mixed solutes
Edouard Moine, Romain Privat, Baptiste Sirjean, and Jean-Noël Jaubert. 2017 · 2017
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Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data
Wen Zhang, Yanlin Chen, Feng Liu, Fei Luo, Gang Tian, and Xiaohong Li. 2017 · 2017
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Deep learning improves prediction of drug–drug and drug–food interactions
Jae Yong Ryu, Hyun Uk Kim, and Sang Yup Lee. 2018 · 2018
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Stanford biomedical network dataset collection
M Zitnik, R Sosi, S Maheshwari, and J Leskovec. 2018 · 2018
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Origins of complex solvent effects on chemical reactivity and computational tools to investigate them: a review
Jithin John Varghese and Samir H Mushrif. 2019 · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019 · 2019
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Experimental database of optical properties of organic compounds
Joonyoung F Joung, Minhi Han, Minseok Jeong, and Sungnam Park. 2020 · 2020
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Kgnn: Knowledge graph neural network for drug-drug interaction prediction
Xuan Lin, Zhe Quan, Zhi-Jie Wang, Tengfei Ma, and Xiangxiang Zeng. 2020 · 2020
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Parameterized explainer for graph neural network
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang. 2020 · 2020
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Chemically interpretable graph interaction network for prediction of pharmacokinetic properties of drug-like molecules
Yashaswi Pathak, Siddhartha Laghuvarapu, Sarvesh Mehta, and U Deva Priyakumar. 2020 · 2020
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Considerations for drug interactions on qtc in exploratory covid-19 treatment
Dan M Roden, Robert A Harrington, Athena Poppas, and Andrea M Russo. 2020 · 2020
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Self-supervised graph transformer on large-scale molecular data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang. 2020 · 2020
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Towards an automatic ai agent for reaction condition recommendation in chemical synthesis
Kexin Chen, Junyou Li, Kunyi Wang, Yuyang Du, Jiahui Yu, Jiamin Lu, Guangyong Chen, Lanqing Li, Jiezhong Qiu, Qun Fang, et al. 2023 · 2023
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Unifying molecular and textual representations via multi-task language modelling
Dimitrios Christofidellis, Giorgio Giannone, Jannis Born, Ole Winther, Teodoro Laino, and Matteo Manica. 2023 · 2023
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Instructblip: Towards general-purpose vision-language models with instruction tuning. arxiv 2023
W Dai, J Li, D Li, AMH Tiong, J Zhao, W Wang, B Li, P Fung, and S Hoi · 2023
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Rumor detection with self-supervised learning on texts and social graph
Yuan Gao, Xiang Wang, Xiangnan He, Huamin Feng, and Yong-Dong Zhang. 2023 · 2023
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Pubchem 2023 update
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Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021 · 2021
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Ssi–ddi: substructure–substructure interactions for drug–drug interaction prediction
Arnold K Nyamabo, Hui Yu, and Jian-Yu Shi. 2021 · 2021
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Solvation effects in organic chemistry: A short historical overview
C. Reichardt. 2021 · 2021
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Transfer learning for solvation free energies: From quantum chemistry to experiments
Florence H Vermeire and William H Green. 2021 · 2021
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Group contribution and machine learning approaches to predict abraham solute parameters, solvation free energy, and solvation enthalpy
Yunsie Chung, Florence H Vermeire, Haoyang Wu, Pierre J Walker, Michael H Abraham, and William H Green. 2022 · 2022
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Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, et al. 2022 · 2022
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Translation between molecules and natural language
Carl Edwards, Tuan Lai, Kevin Ros, Garrett Honke, Kyunghyun Cho, and Heng Ji. 2022 · 2022
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Sunghwan Kim, Jie Chen, Tiejun Cheng, Asta Gindulyte, Jia He, Siqian He, Qingliang Li, Benjamin A. Shoemaker, Paul A. Thiessen, Bo Yu, Leonid Zaslavsky, Jian Zhang, and Evan E. Bolton. 2023 · 2023
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nach0: Multimodal natural and chemical languages foundation model
Micha Livne, Zulfat Miftahutdinov, Elena Tutubalina, Maksim Kuznetsov, Daniil Polykovskiy, Annika Brundyn, Aastha Jhunjhunwala, Anthony Costa, Alex Aliper, and Alex Zhavoronkov. 2023 · 2023
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Something for nothing: Improved solvation free energy prediction with learning
Fanwang Meng, Hanwen Zhang, Juan Samuel Collins-Ramirez, and Paul W. Ayers. 2023 · 2023
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Qizhi Pei, Wei Zhang, Jinhua Zhu, Kehan Wu, Kaiyuan Gao, Lijun Wu, Yingce Xia, and Rui Yan. 2023 · 2023
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Reactiont5: a large-scale pre-trained model towards application of limited reaction data
Tatsuya Sagawa and Ryosuke Kojima. 2023 · 2023
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Relm: Leveraging language models for enhanced chemical reaction prediction
Yaorui Shi, An Zhang, Enzhi Zhang, Zhiyuan Liu, and Xiang Wang. 2023 · 2023
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Large language model can interpret latent space of sequential recommender
Zhengyi Yang, Jiancan Wu, Yanchen Luo, Jizhi Zhang, Yancheng Yuan, An Zhang, Xiang Wang, and Xiangnan He. 2023 · 2023
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Towards robust fidelity for evaluating explainability of graph neural networks
Xu Zheng, Farhad Shirani, Tianchun Wang, Wei Cheng, Zhuomin Chen, Haifeng Chen, Hua Wei, and Dongsheng Luo. 2023 · 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 · 2023
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Exgc: Bridging efficiency and explainability in graph condensation
Junfeng Fang, Xinglin Li, Yongduo Sui, Yuan Gao, Guibin Zhang, Kun Wang, Xiang Wang, and Xiangnan He. 2024 · 2024
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Graph anomaly detection with bi-level optimization
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3d-molm: Towards 3d molecule-text interpretation in language models
Sihang Li, Zhiyuan Liu, Yanchen Luo, Xiang Wang, Xiangnan He, Kenji Kawaguchi, Tat-Seng Chua, and Qi Tian. 2024 · 2024
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Text-free multi-domain graph pre-training: Toward graph foundation models
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Extracting protein-protein interactions (ppis) from biomedical literature using attention-based relational context information
Gilchan Park, Sean McCorkle, Carlos Soto, Ian Blaby, and Shinjae Yoo. 2022 · 2061
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