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Perspective

The power of deep learning to ligand-based novel drug discovery

Pages 755-764 | Received 24 Dec 2019, Accepted 17 Mar 2020, Published online: 31 Mar 2020

References

  • Gasteiger J, Zupan J. Neural networks in chemistry. Angew Chem Int Ed Engl. 1993;105(4):503–527.
  • Halberstam NM, Baskin II, Palyulin VA, et al. Neural networks as a method for elucidating structure-property relationships for organic compounds. Russ Chem Rev. 2003;72(7):629–649.
  • Baskin II, Palyulin VA, Zefirov NS. Neural networks in building QSAR models. Methods Mol Biol. 2008;458:137–158.
  • Baskin II, Winkler D, Tetko IV. A renaissance of neural networks in drug discovery. Expert Opin Drug Discov. 2016;11(8):785–795.
  • LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–444. .
  • Varnek A, Baskin I. Machine learning methods for property prediction in chemoinformatics: quo vadis? J Chem Inf Mod. 2012;52(6): 1413–1437. .
  • Gawehn E, Hiss JA, Schneider G. deep learning in drug discovery. Mol Inf. 2016;35(1):3–14.
  • Ekins S. The next era: deep learning in pharmaceutical research. Pharmaceut Res. 2016;33(11):2594–2603.
  • Carpenter KA, Cohen DS, Jarrell JT, et al. Deep learning and virtual drug screening. Future Med Chem. 2018;10(21):2557–2567.
  • Chen H, Engkvist O, Wang Y, et al. The rise of deep learning in drug discovery. Drug Discov Today. 2018;23(6):1241–1250.
  • Jørgensen PB, Schmidt MN, Winther O. Deep generative models for molecular science. Mol Inf. 2018;37(1–2):1700133.
  • Bajorath J. Data analytics and deep learning in medicinal chemistry. Future Med Chem. 2018;10(13):1541–1543.
  • Tang W, Chen J, Wang Z, et al. Deep learning for predicting toxicity of chemicals: a mini review. J Environ Sci Health Part C. 2018;36(4):252–271.
  • Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: generative models for matter engineering. Science. 2018;361(6400):360–365.
  • Ghasemi F, Mehridehnavi A, Pérez-Garrido A, et al. Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks. Drug Discov Today. 2018;23(10):1784–1790.
  • Jing Y, Bian Y, Hu Z, et al. Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era. Aaps J. 2018;20(3):58.
  • Rifaioglu AS, Atas H, Martin MJ, et al. Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases. Brief Bioinform. 2018;20(5):1878–1912.
  • Sellwood MA, Ahmed M, Segler MH, et al. Artificial intelligence in drug discovery. Future Med Chem. 2018;10(17):2025–2028.
  • Zhavoronkov A. Artificial intelligence for drug discovery, biomarker development, and generation of novel chemistry. Mol Pharm. 2018;15(10):4311–4313.
  • Elton DC, Boukouvalas Z, Fuge MD, et al. Deep learning for molecular design—a review of the state of the art. Mol Syst Design Eng. 2019;4(4):828–849.
  • Schneider P, Walters WP, Plowright AT, et al. Rethinking drug design in the artificial intelligence era. Nat Rev Drug Discovery. 2019. DOI:10.1038/s41573-019-0050-3.
  • Xue D, Gong Y, Yang Z, et al. Advances and challenges in deep generative models for de novo molecule generation. Wiley Interdiscip Rev Comput Mol Sci. 2019;9(3):e1395.
  • Xu Y, Lin K, Wang S, et al. Deep learning for molecular generation. Future Med Chem. 2019;11(6):567–597. .
  • Lake F. Artificial intelligence in drug discovery: what is new, and what is next? Fut Drug Discov. 2019;1(2):FDD19.
  • Walters WP, Stahl MT, Murcko MA. Virtual screening - an overview. Drug Discov Today. 1998;3(4):160–178.
  • Green DV. Virtual screening of virtual libraries. Prog Med Chem. 2003;41:61–97.
  • Ripphausen P, Nisius B, Bajorath J. State-of-the-art in ligand-based virtual screening. Drug Discov Today. 2011;16(9–10):372–376.
  • Bengio Y, Courville A, Vincent P. Representation learning: A review and new perspectives. IEEE Trans Pattern Analys Mach Inte. 2013;35(8):1798–1828.
  • Dahl GE, Jaitly N, Salakhutdinov R. Multi-task neural networks for QSAR predictions. arXiv preprint arXiv:14061231. 2014.
  • Mayr A, Klambauer G, Unterthiner T, et al. DeepTox: toxicity prediction using deep learning. Front Environ Sci. 2016;3(80). DOI:10.3389/fenvs.2015.00080.
  • Lenselink EB, Ten Dijke N, Bongers B, et al. Beyond the hype: deep neural networks outperform established methods using a ChEMBL bioactivity benchmark set. J Cheminf. 2017;9(1):45.
  • Mayr A, Klambauer G, Unterthiner T, et al. Large-scale comparison of machine learning methods for drug target prediction on ChEMBL. Chem Sci. 2018;9(24):5441–5451.
  • Caruana R. Multitask Learning. Mach Learn. 1997;28(1):41–75.
  • Varnek A, Gaudin C, Marcou G, et al. Inductive transfer of knowledge: application of multi-task learning and feature net approaches to model tissue-air partition coefficients. J Chem Inf Mod. 2009;49(1): 133–144. .
  • Xu Y, Ma J, Liaw A, et al. Demystifying multitask deep neural networks for quantitative structure–activity relationships. J Chem Inf Mod. 2017;57(10):2490–2504.
  • Sosnin S, Vashurina M, Withnall M, et al. A survey of multi-task learning methods in chemoinformatics. Mol Inf. 2019;38(4):1800108.
  • Baskin II, Palyulin VA, Zefirov NS. A neural device for searching direct correlations between structures and properties of chemical compounds. J Chem Inf Comput Sci. 1997;37(4):715–721.
  • Weininger D. SMILES, a chemical language and information system. 1. introduction to methodology and encoding rules. J Chem Inf Comput Sci. 1988;28(1):31–36.
  • Kwon S, Yoon S. End-to-End Representation Learning for Chemical-Chemical Interaction Prediction. IEEE/ACM Trans Comput Biol Bioinform. 2019;16(5):1436–1447.
  • Goh GB, Siegel C, Vishnu A, et al. Chemception: a deep neural network with minimal chemistry knowledge matches the performance of expert-developed QSAR/QSPR models. ArXiv Preprint, 2017;arXiv:170606689..
  • Fernandez M, Ban F, Woo G, et al. Toxic colors: the use of deep learning for predicting toxicity of compounds merely from their graphic images. J Chem Inf Mod. 2018;58(8):1533–1543.
  • Xu Y, Chen P, Lin X, et al. Discovery of CDK4 inhibitors by convolutional neural networks. Future Med Chem. 2019;11(3):165–177.
  • Sosnin S, Misin M, Palmer DS, et al. 3D matters! 3D-RISM and 3D convolutional neural network for accurate bioaccumulation prediction. J Phys. 2018;30(32):32LT03.
  • Kuzminykh D, Polykovskiy D, Kadurin A, et al. 3D molecular representations based on the wave transform for convolutional neural networks. Mol Pharm. 2018;15(10):4378–4385.
  • Duvenaud DK, Maclaurin D, Iparraguirre J, et al. Convolutional networks on graphs for learning molecular fingerprints. Advances in Neural Information Processing Systems 28 (NIPS 2015). Montreal, Canada; 2015, p. 2215–2223.
  • Kearnes S, McCloskey K, Berndl M, et al. Molecular graph convolutions: moving beyond fingerprints. J Comput-Aided Mol Des. 2016;30(8):595–608.
  • Coley CW, Barzilay R, Green WH, et al. Convolutional embedding of attributed molecular graphs for physical property prediction. J Chem Inf Mod. 2017;57(8):1757–1772.
  • Xu Y, Pei J, Lai L. Deep learning based regression and multiclass models for acute oral toxicity prediction with automatic chemical feature extraction. J Chem Inf Mod. 2017;57(11):2672–2685.
  • Ståhl N, Falkman G, Karlsson A, et al. Deep convolutional neural networks for the prediction of molecular properties: challenges and opportunities connected to the data. J Integr Bioinform. 2018;16(1):20180065.
  • Michael W, Edvard L, Ola E, et al. Building attention and edge convolution neural networks for bioactivity and physical-chemical property prediction; 2019.
  • Altae-Tran H, Ramsundar B, Pappu AS, et al. Low data drug discovery with one-shot learning. ACS Cent Sci. 2017;3(4):283–293.
  • Baskin II. Is one-shot learning a viable option in drug discovery? Expert Opin Drug Discov. 2019;14(7):601–603.
  • Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–1780. .
  • Chung J, Gülçehre Ç, Cho K, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling. ArXiv Preprint, 2014;arXiv:14123555..
  • Goh GB, Hodas NO, Siegel C, et al. Smiles2vec: an interpretable general-purpose deep neural network for predicting chemical properties. ArXiv Preprint, 2017;arXiv:1712.02034.
  • Ertl P, Lewis R, Martin E, et al. In silico generation of novel, drug-like chemical matter using the LSTM neural network. ArXiv Preprint, 2017;arXiv:171207449.
  • Anvita GT, MA H, HBJ A, et al. Generative recurrent networks for de novo drug design. Mol Inf. 2018;37(1–2):1700111.
  • Sutton RS, Barto AG. Reinforcement learning: an introduction. Cambridge ed. Cambridge: MIT Press; 1998.
  • Popova M, Isayev O, Tropsha A. Deep reinforcement learning for de novo drug design. Sci Adv. 2018;4(7):eaap7885.
  • Olivecrona M, Blaschke T, Engkvist O, et al. Molecular de-novo design through deep reinforcement learning. J Cheminf. 2017;9(1):48.
  • Popova M, Isayev O, Tropsha A Deep reinforcement learning for de-novo drug design; 2017.
  • Neil D, Segler M, Guasch L, et al. Exploring deep recurrent models with reinforcement learning for molecule design. 2018.
  • Putin E, Asadulaev A, Ivanenkov Y, et al. Reinforced adversarial neural computer for de novo molecular design. J Chem Inf Mod. 2018;58(6):1194–1204.
  • Zhou Z, Kearnes S, Li L, et al. Optimization of molecules via deep reinforcement learning. Sci Rep. 2019;9(1):10752.
  • Karpov PV, Osolodkin DI, Baskin II, et al. One-class classification as a novel method of ligand-based virtual screening: the case of glycogen synthase kinase 3ОІ inhibitors. Bioorg Med Chem Lett. 2011;21(22):6728–6731.
  • Zhokhova NI, Baskin II. Energy-based neural networks as a tool for harmony-based virtual screening. Mol Inf. 2017;36(11):1700054.
  • Sutskever I, Vinyals O, Le QV Sequence to sequence learning with neural networks. Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2; Montreal, Canada. 2969173: MIT Press; 2014. p. 3104–3112.
  • Xu Z, Wang S, Zhu F, et al. Seq2seq fingerprint: an unsupervised deep molecular embedding for drug discovery. Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology,and Health Informatics; Boston, Massachusetts, USA. 3107424: ACM; 2017. p. 285–294.
  • Sattarov B, Baskin II, Horvath D, et al. De novo molecular design by combining deep autoencoder recurrent neural networks with generative topographic mapping. J Chem Inf Mod. 2019;59(3):1182–1196.
  • Kingma DP, Welling M. Auto-encoding variational bayes. ArXiv Preprint. 2014;arXiv:13126114.
  • Gómez-Bombarelli R, Wei JN, Duvenaud D, et al. Automatic chemical design using a data-driven continuous representation of molecules. ACS Cent Sci. 2018;4(2):268–276. .
  • Lim J, Ryu S, Kim JW, et al. Molecular generative model based on conditional variational autoencoder for de novo molecular design. J Cheminf. 2018;10(1):31.
  • Kang S, Cho K. Conditional molecular design with deep generative models. J Chem Inf Mod. 2019;59(1):43–52.
  • Zhavoronkov A, Ivanenkov YA, Aliper A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol. 2019;37(9):1038–1040. .
  • Goodfellow IJ, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Arxiv Preprint. 2014;arXiv:14062661.
  • Kadurin A, Aliper A, Kazennov A, et al. The cornucopia of meaningful leads: applying deep adversarial autoencoders for new molecule development in oncology. Oncotarget. 2017;8(7):10883–10890. .
  • Blaschke T, Olivecrona M, Engkvist O, et al. Application of generative autoencoder in de novo molecular design. Mol Inf. 2017;36:1700123.
  • Rasmussen CE, Williams CKI. Gaussian processes in machine learning. Cambridge, Massachusetts: The MIT Press; 2006. ( Dietterich T, editor).
  • Prykhodko O, Johansson SV, Kotsias P-C, et al. A de novo molecular generation method using latent vector based generative adversarial network. J Cheminf. 2019;11(1):74.
  • Guimaraes GL, Sanchez-Lengeling B, Farias PLC, et al. Objective-reinforced generative adversarial networks (organ) for sequence generation models. ArXiv Preprint. 2017;arXiv:170510843.
  • Jin W, Barzilay R, Jaakkola T. Junction Tree Variational Autoencoder for Molecular Graph Generation. ArXiv Preprint. 2018;arXiv:180204364.
  • Simonovsky M, Komodakis N. GraphVAE: towards Generation of Small Graphs Using Variational Autoencoders. ArXiv Preprint. 2018;arXiv:180203480.
  • Samanta B, De A, Jana G, et al. NeVAE: a deep generative model for molecular graphs. Proceedings of the AAAI Conference on Artificial Intelligence. 2019:33(1):1110–1117.
  • De Cao N, Kipf T. MolGAN: an implicit generative model for small molecular graphs. ArXiv Preprint. 2018;arXiv:180511973.
  • Popova M, Shvets M, Oliva J, et al. MolecularRNN: generating realistic molecular graphs with optimized properties. ArXiv Preprint. 2019;arXiv:190513372.
  • Kobyzev I, Prince S, Brubaker MA. Normalizing flows: introduction and ideas. arXiv Preprint. 2019;arXiv:190809257.
  • Papamakarios G, Nalisnick E, Rezende DJ, et al. Normalizing Flows for Probabilistic Modeling and Inference. arXiv Preprint. 2019;arXiv:191202762.
  • Madhawa K, Ishiguro K, Nakago K, et al. An invertible flow model for generating molecular graphs. arXiv Preprint. 2019;arXiv:190511600.
  • Liu J, Kumar A, Ba J, et al. Graph normalizing flows. Advances in Neural Information Processing Systems 32 (NIPS 2019). Curran Associates, Inc; 2019, p. 13578–13588.
  • Shi C, Xu M, Zhu Z, et al. GraphAF: a flow-based autoregressive model for molecular graph generation. arXiv Preprint. 2020;arXiv:200109382.
  • Bjerrum E, Sattarov B. Improving chemical autoencoder latent space and molecular de novo generation diversity with heteroencoders. Biomolecules. 2018;8(4):131.
  • Kusner MJ, Paige B, Hern M, et al. Grammar variational autoencoder. Proceedings of the 34th International Conference on Machine Learning - Volume 70; Sydney, NSW, Australia. JMLR.org; 2017. p. 1945–1954.
  • Noel OB, Andrew D DeepSMILES: an adaptation of smiles for use in machine-learning of chemical structures; 2018.
  • Krenn M, Häse F, Nigam A, et al. SELFIES: a robust representation of semantically constrained graphs with an example application in chemistry. ArXiv Preprint. 2019;arXiv: 190513741.
  • Nigam A, Friederich P, Krenn M, et al. Augmenting genetic algorithms with deep neural networks for exploring the chemical space. arXiv Preprint. 2019;arXiv:190911655.
  • Brown N, Fiscato M, Segler MHS, et al. GuacaMol: benchmarking models for de novo molecular design. J Chem Inf Mod. 2019;59(3):1096–1108.
  • Polykovskiy D, Zhebrak A, Sanchez-Lengeling B, et al. Molecular sets (MOSES): a benchmarking platform for molecular generation models. ArXiv Preprint. 2018;arXiv: 181112823.
  • Preuer K, Renz P, Unterthiner T, et al. Fréchet chemnet distance: a metric for generative models for molecules in drug discovery. J Chem Inf Mod. 2018;58(9):1736–1741.
  • Baskin II, Ait AO, Halberstam NM, et al. An approach to the interpretation of backpropagation neural network models in QSAR studies. SAR QSAR Environ Res. 2002;13(1):35–41.
  • Imrie F, Bradley AR, van der Schaar M, et al. Deep generative models for 3D compound design. BioRxiv Preprint. 2019;bioRxiv:830497.
  • Grow C, Gao K, Nguyen DD, et al. Generative network complex (GNC) for drug discovery. arXiv Preprint. 2019;arXiv:191014650.
  • Gebauer N, Gastegger M, Schütt KT. Generating equilibrium molecules with deep neural networks. ArXiv Preprint. 2018;arXiv:181011347.
  • Gebauer N, Gastegger M, Schütt KT. Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules. ArXiv Preprint. 2019;arXiv:190600957.
  • Zankov DV, Madzhidov TI, Rakhimbekova A, et al. Conjugated quantitative structure–property relationship models: application to simultaneous prediction of tautomeric equilibrium constants and acidity of molecules. J Chem Inf Mod. 2019;59(11):4569–4576.
  • Baskin II, Halberstam NM, Mukhina TV, et al. The learned symmetry concept in revealing quantitative structure-activity relationships with artificial neural networks. SAR QSAR Environ Res. 2001;12(4):401–416.
  • Ertl P, Schuffenhauer A. Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions. J Cheminf. 2009;1(1):8.
  • Coley CW, Rogers L, Green WH, et al. SCScore: synthetic complexity learned from a reaction corpus. J Chem Inf Mod. 2018;58(2):252–261.
  • Baskin II, Madzhidov TI, Antipin IS, et al. Artificial intelligence in synthetic chemistry: achievements and prospects. Russ Chem Rev. 2017;86(11):1127–1156. .
  • Liu B, Ramsundar B, Kawthekar P, et al. Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS Cent Sci. 2017;3(10):1103–1113. .
  • Segler MHS, Preuss M, Waller MP. Planning chemical syntheses with deep neural networks and symbolic AI. Nature. 2018;555:604. .
  • Coley CW, Green WH, Jensen KF. Machine learning in computer-aided synthesis planning. Acc Chem Res. 2018;51(5):1281–1289.
  • Button A, Merk D, Hiss JA, et al. Automated de novo molecular design by hybrid machine intelligence and rule-driven chemical synthesis. Nat Mach Intelligen. 2019;1(7): 307–315. .

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