暂无分享,去创建一个
Regina Barzilay | Tommi S. Jaakkola | Wengong Jin | Kevin Yang | Hua Gao | Miriam Mathea | Andrew Palmer | Brian Kelley | Connor W. Coley | Volker Settels | Angel Guzman-Perez | Timothy Hopper | Kyle Swanson | Philipp Eiden | Klavs Jensen | T. Jaakkola | R. Barzilay | K. Jensen | Kyle Swanson | Wengong Jin | M. Mathea | Kevin Yang | Hua Gao | A. Guzman-Perez | Philipp Eiden | Timothy Hopper | Brian Kelley | Andrew Palmer | Volker Settels
[1] Tatsuya Takagi,et al. Mordred: a molecular descriptor calculator , 2018, Journal of Cheminformatics.
[2] Pierre Baldi,et al. Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules , 2013, J. Chem. Inf. Model..
[3] Lei Jia,et al. Chemi-net: a graph convolutional network for accurate drug property prediction , 2018, ArXiv.
[4] David Rogers,et al. Extended-Connectivity Fingerprints , 2010, J. Chem. Inf. Model..
[5] Klaus-Robert Müller,et al. SchNet: A continuous-filter convolutional neural network for modeling quantum interactions , 2017, NIPS.
[6] Bing Huang,et al. Machine learning prediction errors better than DFT accuracy , 2017, 1702.05532.
[7] Brian K. Shoichet,et al. ZINC - A Free Database of Commercially Available Compounds for Virtual Screening , 2005, J. Chem. Inf. Model..
[8] Manuela Pavan,et al. DRAGON SOFTWARE: AN EASY APPROACH TO MOLECULAR DESCRIPTOR CALCULATIONS , 2006 .
[9] Katsuhiko Ishiguro,et al. Graph Warp Module: an Auxiliary Module for Boosting the Power of Graph Neural Networks , 2019, ArXiv.
[10] Regina Barzilay,et al. Junction Tree Variational Autoencoder for Molecular Graph Generation , 2018, ICML.
[11] Gang Fu,et al. PubChem Substance and Compound databases , 2015, Nucleic Acids Res..
[12] James G. Nourse,et al. Reoptimization of MDL Keys for Use in Drug Discovery , 2002, J. Chem. Inf. Comput. Sci..
[13] Vijay S. Pande,et al. MoleculeNet: a benchmark for molecular machine learning , 2017, Chemical science.
[14] Regina Barzilay,et al. Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction , 2017, J. Chem. Inf. Model..
[15] Luca Antiga,et al. Automatic differentiation in PyTorch , 2017 .
[16] Tatsuya Akutsu,et al. Extensions of marginalized graph kernels , 2004, ICML.
[17] Matt J. Kusner,et al. Grammar Variational Autoencoder , 2017, ICML.
[18] Alexandre Tkatchenko,et al. Quantum-chemical insights from deep tensor neural networks , 2016, Nature Communications.
[19] Chris Hans. Bayesian lasso regression , 2009 .
[20] Nir Friedman,et al. Probabilistic Graphical Models - Principles and Techniques , 2009 .
[21] Pierre Baldi,et al. Kernels for small molecules and the prediction of mutagenicity, toxicity and anti-cancer activity , 2005, ISMB.
[22] Joan Bruna,et al. Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.
[23] Ah Chung Tsoi,et al. The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.
[24] David Weininger,et al. SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..
[25] Xavier Bresson,et al. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.
[26] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[27] J S Smith,et al. ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost , 2016, Chemical science.
[28] David A. Price,et al. Ligand biological activity predicted by cleaning positive and negative chemical correlations , 2019, Proceedings of the National Academy of Sciences.
[29] Dong-Sheng Cao,et al. ChemoPy: freely available python package for computational biology and chemoinformatics , 2013, Bioinform..
[30] Alán Aspuru-Guzik,et al. Convolutional Networks on Graphs for Learning Molecular Fingerprints , 2015, NIPS.
[31] Alessandro Sperduti,et al. Pre-training Graph Neural Networks with Kernels , 2018, ArXiv.
[32] William Stafford Noble,et al. Support vector machine , 2013 .
[33] Pierre Baldi,et al. Influence Relevance Voting: An Accurate And Interpretable Virtual High Throughput Screening Method , 2009, J. Chem. Inf. Model..
[34] Regina Barzilay,et al. Learning Multimodal Graph-to-Graph Translation for Molecular Optimization , 2018, ICLR.
[35] Alán Aspuru-Guzik,et al. Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.
[36] Robert P. Sheridan,et al. Deep Neural Nets as a Method for Quantitative Structure-Activity Relationships , 2015, J. Chem. Inf. Model..
[37] Joan Bruna,et al. Deep Convolutional Networks on Graph-Structured Data , 2015, ArXiv.
[38] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[39] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[40] Thomas G. Dietterich. Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.
[41] Nando de Freitas,et al. Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.
[42] Vijay S. Pande,et al. Molecular graph convolutions: moving beyond fingerprints , 2016, Journal of Computer-Aided Molecular Design.
[43] Mario Medvedovic,et al. LRpath: a logistic regression approach for identifying enriched biological groups in gene expression data , 2009, Bioinform..
[44] Yang Li,et al. PotentialNet for Molecular Property Prediction , 2018, ACS central science.
[45] Hugo Ceulemans,et al. Large-scale comparison of machine learning methods for drug target prediction on ChEMBL , 2018, Chemical science.
[46] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[47] Regina Barzilay,et al. Deriving Neural Architectures from Sequence and Graph Kernels , 2017, ICML.
[48] Andreas Verras,et al. Is Multitask Deep Learning Practical for Pharma? , 2017, J. Chem. Inf. Model..
[49] Le Song,et al. Discriminative Embeddings of Latent Variable Models for Structured Data , 2016, ICML.
[50] Jure Leskovec,et al. Inductive Representation Learning on Large Graphs , 2017, NIPS.
[51] Razvan Pascanu,et al. Interaction Networks for Learning about Objects, Relations and Physics , 2016, NIPS.
[52] Lawrence E. Barker,et al. Logit Models From Economics and Other Fields , 2005, Technometrics.
[53] Risi Kondor,et al. Covariant Compositional Networks For Learning Graphs , 2018, ICLR.
[54] Robert P. Sheridan,et al. Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction , 2013, J. Chem. Inf. Model..
[55] Abhinav Vishnu,et al. Using Rule-Based Labels for Weak Supervised Learning: A ChemNet for Transferable Chemical Property Prediction , 2017, KDD.