Fetching the paper…
Reading the bibliography…
Building in silico models to predict chemical properties and activities is a crucial step in drug discovery.
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Triantafillou, E., Zhu, T., Dumoulin, V., Lamblin, P., Evci, U., Xu, K., Goroshin, R., Gelada, C., Swersky, K., Manzagol, P.-A., and Larochelle, H · 1903
Earlier work this paper cites.
Pre-training Graph Neural Networks
Hu, W., Liu, B., Gomes, J., Zitnik, M., Liang, P., Pande, V., and Leskovec, J · 1905
Earlier work this paper cites.
Sun, F.-Y., Hoffmann, J., Verma, V., and Tang, J · 1908
Earlier work this paper cites.
Learning to Learn
Thrun, S. and Pratt, L. (eds.) · 1998
Earlier work this paper cites.
A Perspective View and Survey of Meta-Learning
Vilalta, R. and Drissi, Y · 2002
Earlier work this paper cites.
ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules
Lusci, A., Pollastri, G., and Baldi, P · 2013
Earlier work this paper cites.
The ChEMBL bioactivity database: an update
Bento, A. P., Gaulton, A., Hersey, A., Bellis, L. J., Chambers, J., Davies, M., Krüger, F. A., Light, Y., Mak, L., McGlinchey, S., Nowotka, M., Papadatos, G., Santos, R., and Overington, J. P · 2014
Earlier work this paper cites.
Learning phrase representations using RNN encoder-decoder for statistical machine translation
Cho, K., Merrienboer, B. v., Gulcehre, C., Bougares, F., Schwenk, H., and Bengio, Y · 2014
Earlier work this paper cites.
Hyperopt: a Python library for model selection and hyperparameter optimization
Bergstra, J., Komer, B., Eliasmith, C., Yamins, D., and Cox, D. D · 2015
Earlier work this paper cites.
Convolutional Networks on Graphs for Learning Molecular Fingerprints
Duvenaud, D., Maclaurin, D., Aguilera-Iparraguirre, J., Gómez-Bombarelli, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
Earlier work this paper cites.
Massively Multitask Networks for Drug Discovery
Ramsundar, B., Kearnes, S., Riley, P., Webster, D., Konerding, D., and Pande, V · 2015
Cited alongside, same era.
Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V., and Riley, P · 2016
Cited alongside, same era.
Matching Networks for One Shot Learning
Vinyals, O., Blundell, C., Lillicrap, T., kavukcuoglu, k., and Wierstra, D · 2016
Cited alongside, same era.
Low Data Drug Discovery with One-Shot Learning
Altae-Tran, H., Ramsundar, B., Pappu, A. S., and Pande, V · 2017
Cited alongside, same era.
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
Finn, C., Abbeel, P., and Levine, S · 2017
Cited alongside, same era.
Neural message passing for Quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
Veličković, P., Fedus, W., Hamilton, W. L., Liò, P., Bengio, Y., and Hjelm, R. D · 2018
Later among the works it cites.
How Powerful are Graph Neural Networks?
Xu, K., Hu, W., Leskovec, J., and Jegelka, S · 2018
Later among the works it cites.
learn2learn, September 2019
Arnold, S. M. R., Mahajan, P., Datta, D., and Ian, B · 2019
Later among the works it cites.
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
Later among the works it cites.
Deep Learning for the Life Sciences
Ramsundar, B., Eastman, P., Walters, P., Pande, V., Leswing, K., and Wu, Z · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Modeling Industrial ADMET Data with Multitask Networks
Kearnes, S., Goldman, B., and Pande, V · 2017
Cited alongside, same era.
Adam: A Method for Stochastic Optimization
Kingma, D. P. and Ba, J · 2017
Cited alongside, same era.
Gated Graph Sequence Neural Networks
Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R · 2017
Cited alongside, same era.
PotentialNet for Molecular Property Prediction
Feinberg, E. N., Sur, D., Wu, Z., Husic, B. E., Mai, H., Li, Y., Sun, S., Yang, J., Ramsundar, B., and Pande, V. S · 2018
Cited alongside, same era.
Vanschoren, J · 2018
Cited alongside, same era.
Predictive Multitask Deep Neural Network Models for ADME-Tox Properties: Learning from Large Data Sets
Wenzel, J., Matter, H., and Schmidt, F · 2019
Later among the works it cites.
Analyzing Learned Molecular Representations for Property Prediction
Yang, K., Swanson, K., Jin, W., Coley, C., Eiden, P., Gao, H., Guzman-Perez, A., Hopper, T., Kelley, B., Mathea, M., Palmer, A., Settels, V., Jaakkola, T., Jensen, K., and Barzilay, R · 2019
Later among the works it cites.
Rapid Learning or Feature Reuse? Towards Understanding the Effectiveness of MAML
Raghu, A., Raghu, M., Bengio, S., and Vinyals, O · 2020
Closest in time.
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Mayr, A., Klambauer, G., Unterthiner, T., Steijaert, M., Wegner, J. K., Ceulemans, H., Clevert, D.-A., and Hochreiter, S · 2041
Closest in time.
MoleculeNet: a benchmark for molecular machine learning
Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., and Pande, V · 2041
Closest in time.