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Current Pharmaceutical Design

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ISSN (Print): 1381-6128
ISSN (Online): 1873-4286

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General Review Article

Recent Progress of Deep Learning in Drug Discovery

Author(s): Feng Wang, XiaoMin Diao, Shan Chang* and Lei Xu*

Volume 27, Issue 17, 2021

Published on: 29 January, 2021

Page: [2088 - 2096] Pages: 9

DOI: 10.2174/1381612827666210129123231

Price: $65

Abstract

Deep learning, an emerging field of artificial intelligence based on neural networks in machine learning, has been applied in various fields and is highly valued. Herein, we mainly review several mainstream architectures in deep learning, including deep neural networks, convolutional neural networks and recurrent neural networks in the field of drug discovery. The applications of these architectures in molecular de novo design, property prediction, biomedical imaging and synthetic planning have also been explored. Apart from that, we further discuss the future direction of the deep learning approaches and the main challenges we need to address.

Keywords: Artificial intelligence, neural networks, deep learning, drug discovery, de novo design, property prediction, biomedical imaging, synthetic planning.

« Previous
[1]
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521(7553): 436-44.
[http://dx.doi.org/10.1038/nature14539] [PMID: 26017442]
[2]
Amari S, Wu S. Improving support vector machine classifiers by modifying kernel functions. Neural Netw 1999; 12(6): 783-9.
[http://dx.doi.org/10.1016/S0893-6080(99)00032-5] [PMID: 12662656]
[3]
Rogers D, Brown RD, Hahn M. Using extended-connectivity fingerprints with Laplacian-modified Bayesian analysis in high-throughput screening follow-up. J Biomol Screen 2005; 10(7): 682-6.
[http://dx.doi.org/10.1177/1087057105281365] [PMID: 16170046]
[4]
Tian S, Sun H, Pan P, et al. Assessing an ensemble docking-based virtual screening strategy for kinase targets by considering protein flexibility. J Chem Inf Model 2014; 54(10): 2664-79.
[http://dx.doi.org/10.1021/ci500414b] [PMID: 25233367]
[5]
Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP. Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 2003; 43(6): 1947-58.
[http://dx.doi.org/10.1021/ci034160g] [PMID: 14632445]
[6]
Cutler DR, Edwards TC Jr, Beard KH, et al. Random forests for classification in ecology. Ecology 2007; 88(11): 2783-92.
[http://dx.doi.org/10.1890/07-0539.1] [PMID: 18051647]
[7]
Ghasemi F, Mehridehnavi A, Pérez-Garrido A, Pérez-Sánchez H. Neural network and deep-learning algorithms used in QSAR studies: merits and drawbacks. Drug Discov Today 2018; 23(10): 1784-90.
[http://dx.doi.org/10.1016/j.drudis.2018.06.016] [PMID: 29936244]
[8]
Mak KK, Pichika MR. Artificial intelligence in drug development: present status and future prospects. Drug Discov Today 2019; 24(3): 773-80.
[http://dx.doi.org/10.1016/j.drudis.2018.11.014] [PMID: 30472429]
[9]
Sanchez-Lengeling B, Aspuru-Guzik A. Inverse molecular design using machine learning: Generative models for matter engineering. Science 2018; 361(6400): 360-5.
[http://dx.doi.org/10.1126/science.aat2663] [PMID: 30049875]
[10]
Mater AC, Coote ML. Deep learning in chemistry. J Chem Inf Model 2019; 59(6): 2545-59.
[http://dx.doi.org/10.1021/acs.jcim.9b00266] [PMID: 31194543]
[11]
Jing Y, Bian Y, Hu Z, Wang L, Xie XQ. Deep learning for drug design: an artificial intelligence paradigm for drug discovery in the big data era. AAPS J 2018; 20(3): 58.
[http://dx.doi.org/10.1208/s12248-018-0210-0] [PMID: 29603063]
[12]
Cao C, Liu F, Tan H, et al. Deep learning and its applications in biomedicine. Genomics Proteomics Bioinformatics 2018; 16(1): 17-32.
[http://dx.doi.org/10.1016/j.gpb.2017.07.003] [PMID: 29522900]
[13]
Schork NJ. Personalized medicine: Time for one-person trials. Nature 2015; 520(7549): 609-11.
[http://dx.doi.org/10.1038/520609a] [PMID: 25925459]
[14]
Miotto R, Wang F, Wang S, Jiang X, Dudley JT. Deep learning for healthcare: review, opportunities and challenges. Brief Bioinform 2018; 19(6): 1236-46.
[http://dx.doi.org/10.1093/bib/bbx044] [PMID: 28481991]
[15]
Ekins S, Puhl AC, Zorn KM, et al. Exploiting machine learning for end-to-end drug discovery and development. Nat Mater 2019; 18(5): 435-41.
[http://dx.doi.org/10.1038/s41563-019-0338-z] [PMID: 31000803]
[16]
Schneider G. Automating drug discovery. Nat Rev Drug Discov 2018; 17(2): 97-113.
[http://dx.doi.org/10.1038/nrd.2017.232] [PMID: 29242609]
[17]
McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. 1943. Bull Math Biol 1990; 52(1-2): 99-115.
[http://dx.doi.org/10.1016/S0092-8240(05)80006-0] [PMID: 2185863]
[18]
Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation Parallel distributed processing. 1986; 1: pp. 318-62..
[19]
Gawehn E, Hiss JA, Schneider G. Deep learning in drug discovery. Mol Inform 2016; 35(1): 3-14.
[http://dx.doi.org/10.1002/minf.201501008] [PMID: 27491648]
[20]
Hirohara M, Saito Y, Koda Y, Sato K, Sakakibara Y. Convolutional neural network based on SMILES representation of compounds for detecting chemical motif. BMC Bioinformatics 2018; 19(19)(Suppl. 19): 526.
[http://dx.doi.org/10.1186/s12859-018-2523-5] [PMID: 30598075]
[21]
LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proc IEEE 1998; 86(11): 2278-324.
[http://dx.doi.org/10.1109/5.726791]
[22]
Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discov Today 2018; 23(6): 1241-50.
[http://dx.doi.org/10.1016/j.drudis.2018.01.039] [PMID: 29366762]
[23]
Polykovskiy D, Zhebrak A, Vetrov D, et al. Entangled conditional adversarial autoencoder for de novo drug discovery. Mol Pharm 2018; 15(10): 4398-405.
[http://dx.doi.org/10.1021/acs.molpharmaceut.8b00839] [PMID: 30180591]
[24]
Kadurin A, Nikolenko S, Khrabrov K, Aliper A, Zhavoronkov A. druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico. Mol Pharm 2017; 14(9): 3098-104.
[http://dx.doi.org/10.1021/acs.molpharmaceut.7b00346] [PMID: 28703000]
[25]
Preuer K, Lewis RPI, Hochreiter S, Bender A, Bulusu KC, Klambauer G. DeepSynergy: predicting anti-cancer drug synergy with Deep Learning. Bioinformatics 2018; 34(9): 1538-46.
[http://dx.doi.org/10.1093/bioinformatics/btx806] [PMID: 29253077]
[26]
Popova M, Isayev O, Tropsha A. Deep reinforcement learning for de novo drug design. Sci Adv 2018; 4(7)eaap7885
[http://dx.doi.org/10.1126/sciadv.aap7885] [PMID: 30050984]
[27]
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): 268-76.
[http://dx.doi.org/10.1021/acscentsci.7b00572] [PMID: 29532027]
[28]
Blaschke T, Olivecrona M, Engkvist O, Bajorath J, Chen H. Application of generative autoencoder in de novo molecular design. Mol Inform 2018; 37(1-2)1700123
[http://dx.doi.org/10.1002/minf.201700123] [PMID: 29235269]
[29]
Goodfellow I, Pougetabadie J, Mirza M, et al. Generative adversarial nets. Neural Information Processing Systems 2014; pp. 2672-80.
[30]
Preuer K, Renz P, Unterthiner T, Hochreiter S, Klambauer G. Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery. J Chem Inf Model 2018; 58(9): 1736-41.
[http://dx.doi.org/10.1021/acs.jcim.8b00234] [PMID: 30118593]
[31]
Segler MHS, Kogej T, Tyrchan C, Waller MP. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent Sci 2018; 4(1): 120-31.
[http://dx.doi.org/10.1021/acscentsci.7b00512] [PMID: 29392184]
[32]
Jaques N, Gu S, Bahdanau D, et al. Sequence tutor: conservative fine-tuning of sequence generation models with KL-control. arXiv: Learning. 2016.
[33]
Olivecrona M, Blaschke T, Engkvist O, Chen H. Molecular de-novo design through deep reinforcement learning. J Cheminform 2017; 9(1): 48.
[http://dx.doi.org/10.1186/s13321-017-0235-x] [PMID: 29086083]
[34]
Shen C, Ding J, Wang Z, et al. From machine learning to deep learning: Advances in scoring functions for protein-ligand docking. WIREs Comput Mol Sci 2020; 10e1429
[http://dx.doi.org/10.1002/wcms.1429]
[35]
Ragoza M, Hochuli J, Idrobo E, Sunseri J, Koes DR. Protein–ligand scoring with convolutional neural networks. J Chem Inf Model 2017; 57(4): 942-57.
[http://dx.doi.org/10.1021/acs.jcim.6b00740] [PMID: 28368587]
[36]
Imrie F, Bradley AR, van der Schaar M, Deane CM. Protein family-specific models using deep neural networks and transfer learning improve virtual screening and highlight the need for more data. J Chem Inf Model 2018; 58(11): 2319-30.
[http://dx.doi.org/10.1021/acs.jcim.8b00350] [PMID: 30273487]
[37]
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 Cheminform 2017; 9(1): 45.
[http://dx.doi.org/10.1186/s13321-017-0232-0] [PMID: 29086168]
[38]
Lusci A, Pollastri G, Baldi P. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J Chem Inf Model 2013; 53(7): 1563-75.
[http://dx.doi.org/10.1021/ci400187y] [PMID: 23795551]
[39]
Korotcov A, Tkachenko V, Russo DP, Ekins S. Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets. Mol Pharm 2017; 14(12): 4462-75.
[http://dx.doi.org/10.1021/acs.molpharmaceut.7b00578] [PMID: 29096442]
[40]
Xu Y, Dai Z, Chen F, Gao S, Pei J, Lai L. Deep learning for drug-induced liver injury. J Chem Inf Model 2015; 55(10): 2085-93.
[http://dx.doi.org/10.1021/acs.jcim.5b00238] [PMID: 26437739]
[41]
Coley CW, Barzilay R, Green WH, Jaakkola TS, Jensen KF. Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction. J Chem Inf Model 2017; 57(8): 1757-72.
[http://dx.doi.org/10.1021/acs.jcim.6b00601] [PMID: 28696688]
[42]
Lynch SR, Bothwell T. A comparison of physical properties, screening procedures and a human efficacy trial for predicting the bioavailability of commercial elemental iron powders used for food fortification. Int J Vitam Nutr Res 2007; 77(2): 107-24.
[http://dx.doi.org/10.1024/0300-9831.77.2.107] [PMID: 17896584]
[43]
Wang N-N, Dong J, Deng Y-H, et al. ADME properties evaluation in drug discovery: prediction of Caco-2 cell permeability using a combination of NSGA-II and boosting. J Chem Inf Model 2016; 56(4): 763-73.
[http://dx.doi.org/10.1021/acs.jcim.5b00642] [PMID: 27018227]
[44]
Hou TJ, Xia K, Zhang W, Xu XJ. ADME evaluation in drug discovery. 4. Prediction of aqueous solubility based on atom contribution approach. J Chem Inf Comput Sci 2004; 44(1): 266-75.
[http://dx.doi.org/10.1021/ci034184n] [PMID: 14741036]
[45]
Prasad S, Brooks BR. A deep learning approach for the blind logP prediction in SAMPL6 challenge. J Comput Aided Mol Des 2020; 34(5): 535-42.
[http://dx.doi.org/10.1007/s10822-020-00292-3] [PMID: 32002779]
[46]
Leach AG, Jones HD, Cosgrove DA, et al. Matched molecular pairs as a guide in the optimization of pharmaceutical properties; a study of aqueous solubility, plasma protein binding and oral exposure. J Med Chem 2006; 49(23): 6672-82.
[http://dx.doi.org/10.1021/jm0605233] [PMID: 17154498]
[47]
Fu L, Liu L, Yang Z-J, et al. Systematic Modeling of log D7.4 Based on Ensemble Machine Learning, Group Contribution, and Matched Molecular Pair Analysis. J Chem Inf Model 2020; 60(1): 63-76.
[http://dx.doi.org/10.1021/acs.jcim.9b00718] [PMID: 31869226]
[48]
Dossetter AG. A statistical analysis of in vitro human microsomal metabolic stability of small phenyl group substituents, leading to improved design sets for parallel SAR exploration of a chemical series. Bioorg Med Chem 2010; 18(12): 4405-14.
[http://dx.doi.org/10.1016/j.bmc.2010.04.077] [PMID: 20510621]
[49]
Lavecchia A. Deep learning in drug discovery: opportunities, challenges and future prospects. Drug Discov Today 2019; 24(10): 2017-32.
[http://dx.doi.org/10.1016/j.drudis.2019.07.006] [PMID: 31377227]
[50]
Mamoshina P, Vieira A, Putin E, Zhavoronkov A. Applications of deep learning in biomedicine. Mol Pharm 2016; 13(5): 1445-54.
[http://dx.doi.org/10.1021/acs.molpharmaceut.5b00982] [PMID: 27007977]
[51]
Kraus OZ, Ba JL, Frey BJ. Classifying and segmenting microscopy images with deep multiple instance learning. Bioinformatics 2016; 32(12): i52-9.
[http://dx.doi.org/10.1093/bioinformatics/btw252] [PMID: 27307644]
[52]
Godinez WJ, Hossain I, Lazic SE, Davies JW, Zhang X. A multi-scale convolutional neural network for phenotyping high-content cellular images. Bioinformatics 2017; 33(13): 2010-9.
[http://dx.doi.org/10.1093/bioinformatics/btx069] [PMID: 28203779]
[53]
Brosch T, Tam R. Manifold learning of brain MRIs by deep learning. Med Image Comput Comput Assist Interv 2013; 16: 633-40.
[http://dx.doi.org/10.1007/978-3-642-40763-5_78]
[54]
Litjens G, Sánchez CI, Timofeeva N, et al. Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci Rep 2016; 6: 26286.
[http://dx.doi.org/10.1038/srep26286] [PMID: 27212078]
[55]
Kleesiek J, Urban G, Hubert A, et al. Deep MRI brain extraction: A 3D convolutional neural network for skull stripping. Neuroimage 2016; 129: 460-9.
[http://dx.doi.org/10.1016/j.neuroimage.2016.01.024] [PMID: 26808333]
[56]
Liu B, Ramsundar B, Kawthekar P, et al. Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS Cent Sci 2017; 3(10): 1103-13.
[http://dx.doi.org/10.1021/acscentsci.7b00303] [PMID: 29104927]
[57]
Savage J, Kishimoto A, Buesser B, et al. Chemical reactant recommendation using a network of organic chemistry. Conference on Recommender Systems. 210-4.
[http://dx.doi.org/ 10.1145/3109859.3109895]
[58]
Segler MHS, Preuss M, Waller MP. Planning chemical syntheses with deep neural networks and symbolic AI. Nature 2018; 555(7698): 604-10.
[http://dx.doi.org/10.1038/nature25978] [PMID: 29595767]
[59]
Wei JN, Duvenaud D, Aspuru-Guzik A. Neural networks for the prediction of organic chemistry reactions. ACS Cent Sci 2016; 2(10): 725-32.
[http://dx.doi.org/10.1021/acscentsci.6b00219] [PMID: 27800555]
[60]
Coley CW, Jin W, Rogers L, et al. A graph-convolutional neural network model for the prediction of chemical reactivity. Chem Sci (Camb) 2018; 10(2): 370-7.
[http://dx.doi.org/10.1039/C8SC04228D] [PMID: 30746086]
[61]
Zhou Z, Li X, Zare RN. Optimizing Chemical Reactions with Deep Reinforcement Learning. ACS Cent Sci 2017; 3(12): 1337-44.
[http://dx.doi.org/10.1021/acscentsci.7b00492] [PMID: 29296675]

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