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How to produce expressive molecular representations is a fundamental challenge in AI-driven drug discovery.
A cluster separation measure
Davies, D. L. & Bouldin, D. W · 1979
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Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
Weininger, D · 1988
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The properties of known drugs. 1. molecular frameworks
Bemis, G. W. & Murcko, M. A · 1996
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Molecular descriptors in chemoinformatics, computational combinatorial chemistry, and virtual screening
Xue, L. & Bajorath, J · 2000
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Computer-aided drug discovery and development (caddd): in silico-chemico-biological approach
Kapetanovic, I · 2008
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ChEMBL: a large-scale bioactivity database for drug discovery
Gaulton, A · 2011
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Drug discovery and development-E-book: technology in transition (Elsevier Health Sciences, 2012)
Hill, R. G · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I. & Hinton, G. E · 2012
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Data-driven prediction of drug effects and interactions
Tatonetti, N. P., Patrick, P. Y., Daneshjou, R. & Altman, R. B · 2012
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Metabolite identification and molecular fingerprint prediction through machine learning
Heinonen, M., Shen, H., Zamboni, N. & Rousu, J · 2012
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Learning word embeddings efficiently with noise-contrastive estimation
Mnih, A. & Kavukcuoglu, K · 2013
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Computational methods in drug discovery
Sliwoski, G., Kothiwale, S., Meiler, J. & Lowe, E. W · 2014
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Cho, K · 2014
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Qsar modeling: where have you been? where are you going to?
Cherkasov, A · 2014
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Similarity-based modeling in large-scale prediction of drug-drug interactions
Vilar, S · 2014
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Drug–target interaction prediction via chemogenomic space: learning-based methods
Mousavian, Z. & Masoudi-Nejad, A · 2014
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Zinc 15 – ligand discovery for everyone
Sterling, T. & Irwin, J. J · 2015
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Massively multitask networks for drug discovery
Ramsundar, B · 2015
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. & Welling, M · 2016
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Computational exploration of molecular scaffolds in medicinal chemistry: Miniperspective
Hu, Y., Stumpfe, D. & Bajorath, J · 2016
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Molecular graph convolutions: moving beyond fingerprints
Kearnes, S., McCloskey, K., Berndl, M., Pande, V. & Riley, P · 2016
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Gaussian error linear units (gelus)
Hendrycks, D. & Gimpel, K · 2016
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P., Vinyals, O. & Dahl, G. E · 2017
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Inductive representation learning on large graphs
Hamilton, W. L., Ying, R. & Leskovec, J · 2017
Cited alongside, same era.
Seq2seq fingerprint: An unsupervised deep molecular embedding for drug discovery
Xu, Z., Wang, S., Zhu, F. & Huang, J · 2017
Cited alongside, same era.
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K · 2017
Cited alongside, same era.
Deep-learning-based drug–target interaction prediction
Wen, M · 2017
Cited alongside, same era.
Modelling chemical reasoning to predict and invent reactions
Segler, M. H. S. & Waller, M. P · 2017
Cited alongside, same era.
Attention is all you need
Vaswani, A · 2017
Cited alongside, same era.
Smiles-bert: large scale unsupervised pre-training for molecular property prediction
Wang, S., Guo, Y., Wang, Y., Sun, H. & Huang, J · 2019
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Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
Winter, R., Montanari, F., Noé, F. & Clevert, D.-A · 2019
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Deep graph infomax (2019)
Veličković, P · 2019
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Sun, F.-Y., Hoffmann, J., Verma, V. & Tang, J · 2019
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Deepgcns: Can gcns go as deep as cnns?
Li, G., Muller, M., Thabet, A. & Ghanem, B · 2019
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Molecular property prediction: A multilevel quantum interactions modeling perspective
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Neural network and deep-learning algorithms used in qsar studies: merits and drawbacks
Ghasemi, F., Mehridehnavi, A., Perez-Garrido, A. & Perez-Sanchez, H · 2018
Cited alongside, same era.
Deep learning improves prediction of drug–drug and drug–food interactions
Ryu, J. Y., Kim, H. U. & Lee, S. Y · 2018
Cited alongside, same era.
Graph attention networks (ICLR, 2018)
Veličković, P · 2018
Cited alongside, same era.
MoleculeNet: A benchmark for molecular machine learning
Wu, Z · 2018
Cited alongside, same era.
Automatic chemical design using a data-driven continuous representation of molecules
Gómez-Bombarelli, R · 2018
Cited alongside, same era.
N-gram graph: Simple unsupervised representation for graphs, with applications to molecules (2018)
Liu, S., Demirel, M. F. & Liang, Y · 2018
Cited alongside, same era.
Lu, C · 2019
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Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism
Xiong, Z · 2019
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N-gram graph: Simple unsupervised representation for graphs, with applications to molecules
Liu, S., Demirel, M. F. & Liang, Y · 2019
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Drug-drug interactions (CRC Press, 2019)
Rodrigues, A. D · 2019
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Ddi-pulearn: a positive-unlabeled learning method for large-scale prediction of drug-drug interactions
Zheng, Y · 2019
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Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
Tsubaki, M., Tomii, K. & Sese, J · 2019
Later among the works it cites.
Deep learning in drug target interaction prediction: Current and future perspective
Abbasi, K., Razzaghi, P., Poso, A., Ghanbari-Ara, S. & Masoudi-Nejad, A · 2020
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Machine learning models for drug–target interactions: current knowledge and future directions
D’Souza, S., Prema, K. & Balaji, S · 2020
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Self-supervised graph transformer on large-scale molecular data
Rong, Y · 2020
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Self-supervised learning: Generative or contrastive
Liu, X · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S. & Girshick, R · 2020
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Transfer learning enables the molecular transformer to predict regio-and stereoselective reactions on carbohydrates
Pesciullesi, G., Schwaller, P., Laino, T. & Reymond, J.-L · 2020
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Chemberta: Large-scale self-supervised pretraining for molecular property prediction
Chithrananda, S., Grand, G. & Ramsundar, B · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M. & Hinton, G · 2020
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Gcc: Graph contrastive coding for graph neural network pre-training
Qiu, J · 2020
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Towards deeper graph neural networks
Liu, M., Gao, H. & Ji, S · 2020
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Caster: Predicting drug interactions with chemical substructure representation
Huang, K., Xiao, C., Hoang, T., Glass, L. & Sun, J · 2020
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