暂无分享,去创建一个
Shuiwang Ji | Maho Nakata | Youzhi Luo | Meng Liu | Xinyi Xu | Xuan Zhang | Yaochen Xie | Zhao Xu | Kaleb Dickerson | Cheng Deng | Shuiwang Ji | Yaochen Xie | Maho Nakata | Zhao Xu | Youzhi Luo | Meng Liu | Xuan Zhang | Xinyi Xu | Kaleb Dickerson | Cheng Deng
[1] Markus Meuwly,et al. PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges. , 2019, Journal of chemical theory and computation.
[2] Y. Bengio,et al. Learning Neural Generative Dynamics for Molecular Conformation Generation , 2021, ICLR.
[3] Charlotte M. Deane,et al. Deep Generative Models for 3D Linker Design , 2020, J. Chem. Inf. Model..
[4] Alán Aspuru-Guzik,et al. Convolutional Networks on Graphs for Learning Molecular Fingerprints , 2015, NIPS.
[5] Bernard Ghanem,et al. DeeperGCN: All You Need to Train Deeper GCNs , 2020, ArXiv.
[6] Jian Tang,et al. Learning Gradient Fields for Molecular Conformation Generation , 2021, ICML.
[7] David Budden,et al. Large-scale graph representation learning with very deep GNNs and self-supervision , 2021, ArXiv.
[8] Noel M. O'Boyle,et al. cclib: A library for package‐independent computational chemistry algorithms , 2008, J. Comput. Chem..
[9] Gang Fu,et al. PubChem Substance and Compound databases , 2015, Nucleic Acids Res..
[10] David Weininger,et al. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..
[11] Shuangjia Zheng,et al. SyntaLinker: automatic fragment linking with deep conditional transformer neural networks , 2020, Chemical Science.
[12] Pietro Liò,et al. Graph Attention Networks , 2017, ICLR.
[13] Jian Tang,et al. An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming , 2021, ICML.
[14] Chenglin Wu,et al. First Place Solution of KDD Cup 2021 & OGB Large-Scale Challenge Graph Prediction Track , 2021, ArXiv.
[15] Jure Leskovec,et al. How Powerful are Graph Neural Networks? , 2018, ICLR.
[16] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[17] Pavlo O. Dral,et al. Quantum chemistry structures and properties of 134 kilo molecules , 2014, Scientific Data.
[18] Shuiwang Ji,et al. Spherical Message Passing for 3D Graph Networks , 2021, ArXiv.
[19] J. Leskovec,et al. Open Graph Benchmark: Datasets for Machine Learning on Graphs , 2020, NeurIPS.
[20] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[21] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[22] J. S. Dixon,et al. Distance Geometry in Molecular Modeling , 2007 .
[23] Maho Nakata,et al. PubChemQC Project: A Large-Scale First-Principles Electronic Structure Database for Data-Driven Chemistry , 2017, J. Chem. Inf. Model..
[24] Shuiwang Ji,et al. Graph U-Nets , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[25] José Miguel Hernández-Lobato,et al. A Generative Model for Molecular Distance Geometry , 2020, ICML.
[26] Shuiwang Ji,et al. Advanced Graph and Sequence Neural Networks for Molecular Property Prediction and Drug Discovery. , 2020, Bioinformatics.
[27] Klaus-Robert Müller,et al. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions , 2017, NIPS.
[28] Shuiwang Ji,et al. Towards Deeper Graph Neural Networks , 2020, KDD.
[29] Jian Sun,et al. Identity Mappings in Deep Residual Networks , 2016, ECCV.
[30] Anders S Christensen,et al. FCHL revisited: Faster and more accurate quantum machine learning. , 2020, The Journal of chemical physics.
[31] Jure Leskovec,et al. OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs , 2021, NeurIPS Datasets and Benchmarks.
[32] Sereina Riniker,et al. Better Informed Distance Geometry: Using What We Know To Improve Conformation Generation , 2015, J. Chem. Inf. Model..
[33] Timothy F. Havel. Distance Geometry: Theory, Algorithms, and Chemical Applications , 2002 .
[34] Vijay S. Pande,et al. MoleculeNet: a benchmark for molecular machine learning , 2017, Chemical science.
[35] Regina Barzilay,et al. Analyzing Learned Molecular Representations for Property Prediction , 2019, J. Chem. Inf. Model..
[36] Jian Sun,et al. Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[37] Elman Mansimov,et al. Molecular Geometry Prediction using a Deep Generative Graph Neural Network , 2019, Scientific Reports.
[38] Vijay S. Pande,et al. Molecular graph convolutions: moving beyond fingerprints , 2016, Journal of Computer-Aided Molecular Design.
[39] Aditya R. Thawani,et al. The Photoswitch Dataset: A Molecular Machine Learning Benchmark for the Advancement of Synthetic Chemistry , 2020, ArXiv.
[40] C. Willmott,et al. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .
[41] Stephan Günnemann,et al. Directional Message Passing for Molecular Graphs , 2020, ICLR.
[42] Emma J. Chory,et al. A Deep Learning Approach to Antibiotic Discovery , 2020, Cell.
[43] Shuiwang Ji,et al. Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks , 2021, ArXiv.
[44] R. Kondor,et al. On representing chemical environments , 2012, 1209.3140.
[45] Maho Nakata,et al. PubChemQC PM6: Data Sets of 221 Million Molecules with Optimized Molecular Geometries and Electronic Properties , 2020, J. Chem. Inf. Model..
[46] Zhengyang Wang,et al. Large-Scale Learnable Graph Convolutional Networks , 2018, KDD.