Masked graph modeling for molecule generation

[1]  G. Klambauer,et al.  Graph networks for molecular design , 2020, Mach. Learn. Sci. Technol..

[2]  Matt J. Kusner,et al.  Barking up the right tree: an approach to search over molecule synthesis DAGs , 2020, NeurIPS.

[3]  Ling Wang,et al.  Heck reaction prediction using a transformer model based on a transfer learning strategy. , 2020, Chemical communications.

[4]  Bo Dai,et al.  Scalable Deep Generative Modeling for Sparse Graphs , 2020, ICML.

[5]  Stanislaw Jastrzebski,et al.  Molecule Attention Transformer , 2020, ArXiv.

[6]  José Miguel Hernández-Lobato,et al.  Reinforcement Learning for Molecular Design Guided by Quantum Mechanics , 2020, ICML.

[7]  Regina Barzilay,et al.  Improving Molecular Design by Stochastic Iterative Target Augmentation , 2020, ICML.

[8]  Kevin Gimpel,et al.  ALBERT: A Lite BERT for Self-supervised Learning of Language Representations , 2019, ICLR.

[9]  Kyunghyun Cho,et al.  Latent-Variable Non-Autoregressive Neural Machine Translation with Deterministic Inference using a Delta Posterior , 2019, AAAI.

[10]  Pascal Friederich,et al.  Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation , 2019, Mach. Learn. Sci. Technol..

[11]  J. Leskovec,et al.  Strategies for Pre-training Graph Neural Networks , 2019, ICLR.

[12]  Seokho Kang,et al.  Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation , 2019, Journal of Cheminformatics.

[13]  Renjie Liao,et al.  Efficient Graph Generation with Graph Recurrent Attention Networks , 2019, NeurIPS.

[14]  Omer Levy,et al.  RoBERTa: A Robustly Optimized BERT Pretraining Approach , 2019, ArXiv.

[15]  J. Reymond,et al.  Randomized SMILES strings improve the quality of molecular generative models , 2019, Journal of Cheminformatics.

[16]  Alex Wang,et al.  A Generalized Framework of Sequence Generation with Application to Undirected Sequence Models , 2019, ArXiv.

[17]  José Miguel Hernández-Lobato,et al.  A COLD Approach to Generating Optimal Samples , 2019, ArXiv.

[18]  Omer Levy,et al.  SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems , 2019, NeurIPS.

[19]  Aviral Kumar,et al.  Graph Normalizing Flows , 2019, NeurIPS.

[20]  Omer Levy,et al.  Mask-Predict: Parallel Decoding of Conditional Masked Language Models , 2019, EMNLP.

[21]  Zois Boukouvalas,et al.  Deep learning for molecular generation and optimization - a review of the state of the art , 2019, Molecular Systems Design & Engineering.

[22]  Alex Wang,et al.  BERT has a Mouth, and It Must Speak: BERT as a Markov Random Field Language Model , 2019, Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation.

[23]  Jakob Uszkoreit,et al.  Insertion Transformer: Flexible Sequence Generation via Insertion Operations , 2019, ICML.

[24]  Kyunghyun Cho,et al.  Non-Monotonic Sequential Text Generation , 2019, ICML.

[25]  Qi Liu,et al.  Insertion-based Decoding with Automatically Inferred Generation Order , 2019, Transactions of the Association for Computational Linguistics.

[26]  Guillaume Lample,et al.  Cross-lingual Language Model Pretraining , 2019, NeurIPS.

[27]  Kyunghyun Cho,et al.  Passage Re-ranking with BERT , 2019, ArXiv.

[28]  Marwin H. S. Segler,et al.  GuacaMol: Benchmarking Models for De Novo Molecular Design , 2018, J. Chem. Inf. Model..

[29]  Andrew R. Leach,et al.  ChEMBL: towards direct deposition of bioassay data , 2018, Nucleic Acids Res..

[30]  Jan H. Jensen,et al.  A graph-based genetic algorithm and generative model/Monte Carlo tree search for the exploration of chemical space , 2018, Chemical science.

[31]  Li Li,et al.  Optimization of Molecules via Deep Reinforcement Learning , 2018, Scientific Reports.

[32]  Kyunghyun Cho,et al.  Conditional molecular design with deep generative models , 2018, J. Chem. Inf. Model..

[33]  Stefano Ermon,et al.  Graphite: Iterative Generative Modeling of Graphs , 2018, ICML.

[34]  Ming-Wei Chang,et al.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding , 2019, NAACL.

[35]  Dmitry Vetrov,et al.  Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery. , 2018, Molecular pharmaceutics.

[36]  Percy Liang,et al.  Know What You Don’t Know: Unanswerable Questions for SQuAD , 2018, ACL.

[37]  Jure Leskovec,et al.  Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation , 2018, NeurIPS.

[38]  Nicola De Cao,et al.  MolGAN: An implicit generative model for small molecular graphs , 2018, ArXiv.

[39]  Omer Levy,et al.  GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding , 2018, BlackboxNLP@EMNLP.

[40]  Sepp Hochreiter,et al.  Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery , 2018, J. Chem. Inf. Model..

[41]  Jure Leskovec,et al.  GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models , 2018, ICML.

[42]  Razvan Pascanu,et al.  Learning Deep Generative Models of Graphs , 2018, ICLR 2018.

[43]  Regina Barzilay,et al.  Junction Tree Variational Autoencoder for Molecular Graph Generation , 2018, ICML.

[44]  Nikos Komodakis,et al.  GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders , 2018, ICANN.

[45]  Yibo Li,et al.  Multi-objective de novo drug design with conditional graph generative model , 2018, Journal of Cheminformatics.

[46]  Thierry Kogej,et al.  Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks , 2017, ACS central science.

[47]  Minyi Guo,et al.  GraphGAN: Graph Representation Learning with Generative Adversarial Nets , 2017, AAAI.

[48]  Samuel R. Bowman,et al.  A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference , 2017, NAACL.

[49]  Alán Aspuru-Guzik,et al.  Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.

[50]  Abhinav Vishnu,et al.  ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction , 2017, ArXiv.

[51]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[52]  Bowen Liu,et al.  Retrosynthetic Reaction Prediction Using Neural Sequence-to-Sequence Models , 2017, ACS central science.

[53]  Alán Aspuru-Guzik,et al.  Objective-Reinforced Generative Adversarial Networks (ORGAN) for Sequence Generation Models , 2017, ArXiv.

[54]  Samuel S. Schoenholz,et al.  Neural Message Passing for Quantum Chemistry , 2017, ICML.

[55]  Matt J. Kusner,et al.  Grammar Variational Autoencoder , 2017, ICML.

[56]  George Papadatos,et al.  The ChEMBL database in 2017 , 2016, Nucleic Acids Res..

[57]  Geoffrey E. Hinton,et al.  Layer Normalization , 2016, ArXiv.

[58]  Jian Zhang,et al.  SQuAD: 100,000+ Questions for Machine Comprehension of Text , 2016, EMNLP.

[59]  Samy Bengio,et al.  Order Matters: Sequence to sequence for sets , 2015, ICLR.

[60]  Christopher Potts,et al.  A large annotated corpus for learning natural language inference , 2015, EMNLP.

[61]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[62]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[63]  Pablo Tamayo,et al.  Parallel genome-scale loss of function screens in 216 cancer cell lines for the identification of context-specific genetic dependencies , 2014, Scientific Data.

[64]  Pavlo O. Dral,et al.  Quantum chemistry structures and properties of 134 kilo molecules , 2014, Scientific Data.

[65]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[66]  Daan Wierstra,et al.  Stochastic Backpropagation and Approximate Inference in Deep Generative Models , 2014, ICML.

[67]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[68]  Yoshua Bengio,et al.  What regularized auto-encoders learn from the data-generating distribution , 2012, J. Mach. Learn. Res..

[69]  Jean-Louis Reymond,et al.  Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17 , 2012, J. Chem. Inf. Model..

[70]  Tomas Mikolov,et al.  RNNLM - Recurrent Neural Network Language Modeling Toolkit , 2011 .

[71]  Hugo Larochelle,et al.  The Neural Autoregressive Distribution Estimator , 2011, AISTATS.

[72]  Yoshua Bengio,et al.  Deep Sparse Rectifier Neural Networks , 2011, AISTATS.

[73]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[74]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[75]  Yoshua Bengio,et al.  A Neural Probabilistic Language Model , 2003, J. Mach. Learn. Res..

[76]  Samy Bengio,et al.  Modeling High-Dimensional Discrete Data with Multi-Layer Neural Networks , 1999, NIPS.

[77]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[78]  W. Guida,et al.  The art and practice of structure‐based drug design: A molecular modeling perspective , 1996, Medicinal research reviews.

[79]  R. A. Leibler,et al.  On Information and Sufficiency , 1951 .