Cui Q, Lu S, Ni B, Zeng X, Tan Y, Chen YD, Zhao H (2020) Improved prediction of aqueous solubility of novel compounds by going deeper with deep learning. Front Oncol 10:121
Article PubMed PubMed Central Google Scholar
Wang Y-B, You Z-H, Yang S, Yi H-C, Chen Z-H, Zheng K (2020) A deep learning-based method for drug-target interaction prediction based on long short-term memory neural network. BMC Med Inform Decis Making 20(2):1–9
Yuan Y, Zheng F, Zhan C-G (2018) Improved prediction of blood-brain barrier permeability through machine learning with combined use of molecular property-based descriptors and fingerprints. AAPS J 20(3):1–10
Todeschini R, Consonni V (2008) Handbook of Molecular Descriptors. Wiley
Rogers D, Hahn M (2010) Extended-connectivity fingerprints. J Chem Inf Model 50(5):742–754
Article CAS PubMed Google Scholar
Kearnes S, McCloskey K, Berndl M, Pande V, Riley P (2016) Molecular graph convolutions: moving beyond fingerprints. J Computer-aided Mol Des 30(8):595–608
Duvenaud DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, Aspuru-Guzik A, Adams RP (2015) Convolutional networks on graphs for learning molecular fingerprints. Advances in Neural Information Processing Systems 28
Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. In: International Conference on Machine Learning, pp. 1263–1272. PMLR
Kensert A, Bouwmeester R, Efthymiadis K, Van Broeck P, Desmet G, Cabooter D (2021) Graph convolutional networks for improved prediction and interpretability of chromatographic retention data. Anal Chem 93(47):15633–15641
Article CAS PubMed Google Scholar
Jiang D, Wu Z, Hsieh C-Y, Chen G, Liao B, Wang Z, Shen C, Cao D, Wu J, Hou T (2021) Could graph neural networks learn better molecular representation for drug discovery? a comparison study of descriptor-based and graph-based models. J Cheminform 13(1):1–23
Pope PE, Kolouri S, Rostami M, Martin CE, Hoffmann H (2019) Explainability methods for graph convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10772–10781
Girija SS (2016) Tensorflow: Large-scale machine learning on heterogeneous distributed systems. Software available from tensorflow. org 39(9)
Chollet F, et al (2015) Keras. GitHub. https://github.com/fchollet/keras
Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, et al (2019) Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems 32
Grattarola D, Alippi C (2021) Graph neural networks in tensorflow and keras with spektral [application notes]. IEEE Comput Intell Mag 16(1):99–106
Wang M, Zheng D, Ye Z, Gan Q, Li M, Song X, Zhou J, Ma C, Yu L, Gai Y, et al (2019) Deep graph library: A graph-centric, highly-performant package for graph neural networks. arXiv preprint arXiv:1909.01315
Ramsundar B, Eastman P, Walters P, Pande V, Leswing K, Wu Z (2019) Deep Learning for the Life Sciences. O’Reilly Media, ???
Fey M, Lenssen JE (2019) Fast graph representation learning with pytorch geometric. arXiv preprint arXiv:1903.02428
Battaglia PW, Hamrick JB, Bapst V, Sanchez-Gonzalez A, Zambaldi V, Malinowski M, Tacchetti A, Raposo D, Santoro A, Faulkner R, et al (2018) Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261
Schütt KT, Arbabzadah F, Chmiela S, Müller KR, Tkatchenko A (2017) Quantum-chemical insights from deep tensor neural networks. Nat Commun 8(1):1–8
Welling M, Kipf TN (2016) Semi-supervised classification with graph convolutional networks. In: J. International Conference on Learning Representations (ICLR 2017)
Dwivedi VP, Joshi CK, Laurent T, Bengio Y, Bresson X (2020) Benchmarking graph neural networks. arXiv preprint arXiv:2003.00982
Xu K, Hu W, Leskovec J, Jegelka S (2018) How powerful are graph neural networks? arXiv preprint arXiv:1810.00826
Velickovic P, Cucurull G, Casanova A, Romero A, Lio P, Bengio Y (2017) Graph attention networks. Stat 1050:20
Monti F, Boscaini D, Masci J, Rodola E, Svoboda J, Bronstein MM (2017) Geometric deep learning on graphs and manifolds using mixture model cnns. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5115–5124
Devlin J, Chang M-W, Lee K, Toutanova K (2018) Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778
Wu Z, Ramsundar B, Feinberg EN, Gomes J, Geniesse C, Pappu AS, Leswing K, Pande V (2018) Moleculenet: a benchmark for molecular machine learning. Chem Sci 9(2):513–530
Article CAS PubMed Google Scholar
Domingo-Almenara X, Guijas C, Billings E, Montenegro-Burke JR, Uritboonthai W, Aisporna AE, Chen E, Benton HP, Siuzdak G (2019) The metlin small molecule dataset for machine learning-based retention time prediction. Nat Commun 10(1):1–9
Bonini P, Kind T, Tsugawa H, Barupal DK, Fiehn O (2020) Retip: retention time prediction for compound annotation in untargeted metabolomics. Anal Chem 92(11):7515–7522
Article CAS PubMed PubMed Central Google Scholar
Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980
Tsugawa H, Kind T, Nakabayashi R, Yukihira D, Tanaka W, Cajka T, Saito K, Fiehn O, Arita M (2016) Hydrogen rearrangement rules: computational ms/ms fragmentation and structure elucidation using ms-finder software. Anal Chem 88(16):7946–7958
Article CAS PubMed PubMed Central Google Scholar
Lai Z, Tsugawa H, Wohlgemuth G, Mehta S, Mueller M, Zheng Y, Ogiwara A, Meissen J, Showalter M, Takeuchi K (2018) Identifying metabolites by integrating metabolome databases with mass spectrometry cheminformatics. Nat Methods 15(1):53–56
Article CAS PubMed Google Scholar
De Cao N, Kipf T (2018) Molgan: An implicit generative model for small molecular graphs. arXiv preprint arXiv:1805.11973
Hu W, Liu B, Gomes J, Zitnik M, Liang P, Pande V, Leskovec J (2019) Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265
Tsugawa H, Kind T, Nakabayashi R, Yukihira D, Tanaka W, Cajka T, Saito K, Fiehn O, Arita M (2016) Hydrogen rearrangement rules: computational ms/ms fragmentation and structure elucidation using ms-finder software. Anal Chem 88(16):7946–7958
Article CAS PubMed PubMed Central Google Scholar
Roberts TC, Langer R, Wood MJ (2020) Advances in oligonucleotide drug delivery. Nat Rev Drug Discov 19(10):673–694
Article CAS PubMed PubMed Central Google Scholar
Kensert A, Desmet G, Cabooter D (2024) A hands-on tutorial on quantitative structure-activity relationships using fully expressive graph neural networks. Analytica Chimica Acta 1331:343046. https://doi.org/10.1016/j.aca.2024.343046
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