Generic placeholder image

Current Topics in Medicinal Chemistry

Editor-in-Chief

ISSN (Print): 1568-0266
ISSN (Online): 1873-4294

Review Article

Precision Psychiatry: Machine Learning as a Tool to Find New Pharmacological Targets

Author(s): João Rema*, Filipa Novais and Diogo Telles-Correia

Volume 22, Issue 15, 2022

Published on: 04 October, 2021

Page: [1261 - 1269] Pages: 9

DOI: 10.2174/1568026621666211004095917

Price: $65

Abstract

Objectives: The present work reviews current evidence regarding the contribution of machine learning to the discovery of new drug targets.

Methods: Scientific articles from PubMed, SCOPUS, EMBASE, and Web of Science Core Collection published until May 2021 were included in this review.

Results: The most significant areas of research are schizophrenia, depression and anxiety, Alzheimer´s disease, and substance use disorders. ML techniques have pinpointed target gene candidates and pathways, new molecular substances, and several biomarkers regarding psychiatric disorders. Drug repositioning studies using ML have identified multiple drug candidates as promising therapeutic agents.

Conclusion: Next-generation ML techniques and subsequent deep learning may power new findings regarding the discovery of new pharmacological agents by bridging the gap between biological data and chemical drug information.

Keywords: Machine learning, Artificial intelligence, Neural networks, Psychiatry, Drugs, Pharmacological targets.

[1]
Karekar, S.R.; Vazifdar, A.K. Current status of clinical research using artificial intelligence techniques: A registry-based audit. Perspect. Clin. Res., 2021, 12(1), 48-52.
[http://dx.doi.org/10.4103/picr.PICR_25_20] [PMID: 33816209]
[2]
Ahmed, Z.; Mohamed, K.; Zeeshan, S.; Dong, X. Artificial Intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020, 2020, baaa010.
[http://dx.doi.org/10.1093/database/baaa010]
[3]
Shah, P.; Kendall, F.; Khozin, S.; Goosen, R.; Hu, J.; Laramie, J.; Ringel, M.; Schork, N. Artificial intelligence and machine learning in clinical development: A translational perspective. NPJ Digit. Med., 2019, 2, 69.
[http://dx.doi.org/10.1038/s41746-019-0148-3] [PMID: 31372505]
[4]
Graham, S.; Depp, C.; Lee, E.E.; Nebeker, C.; Tu, X.; Kim, H-C.; Jeste, D.V. Artificial intelligence for mental health and mental illnesses: An overview. Curr. Psychiatry Rep., 2019, 21(11), 116.
[http://dx.doi.org/10.1007/s11920-019-1094-0] [PMID: 31701320]
[5]
Kendler, K.S. The nature of psychiatric disorders. World Psychiatry, 2016, 15(1), 5-12.
[http://dx.doi.org/10.1002/wps.20292] [PMID: 26833596]
[6]
Kendler, K.S.; Zachar, P.; Craver, C. What kinds of things are psychiatric disorders? Psychol. Med., 2011, 41(6), 1143-1150.
[http://dx.doi.org/10.1017/S0033291710001844] [PMID: 20860872]
[7]
Chekroud, A.M.; Bondar, J.; Delgadillo, J.; Doherty, G.; Wasil, A.; Fokkema, M.; Cohen, Z.; Belgrave, D.; DeRubeis, R.; Iniesta, R.; Dwyer, D.; Choi, K. The promise of machine learning in predicting treatment outcomes in psychiatry. World Psychiatry, 2021, 20(2), 154-170.
[http://dx.doi.org/10.1002/wps.20882] [PMID: 34002503]
[8]
Koppe, G.; Meyer-Lindenberg, A.; Durstewitz, D. Deep learning for small and big data in psychiatry. Neuropsychopharmacology, 2021, 46(1), 176-190.
[http://dx.doi.org/10.1038/s41386-020-0767-z] [PMID: 32668442]
[9]
Durstewitz, D.; Koppe, G.; Meyer-Lindenberg, A. Deep neural networks in psychiatry. Mol. Psychiatry, 2019, 24(11), 1583-1598.
[http://dx.doi.org/10.1038/s41380-019-0365-9] [PMID: 30770893]
[10]
Zhou, Z.; Wu, T-C.; Wang, B.; Wang, H.; Tu, X.M.; Feng, C. Machine learning methods in psychiatry: A brief introduction. Gen. Psychiatr., 2020, 33(1), e100171.
[http://dx.doi.org/10.1136/gpsych-2019-100171] [PMID: 32090196]
[11]
Levchenko, A.; Nurgaliev, T.; Kanapin, A.; Samsonova, A.; Gainetdinov, R.R. Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders. Heliyon, 2020, 6(5), e03990.
[http://dx.doi.org/10.1016/j.heliyon.2020.e03990] [PMID: 32462093]
[12]
Ngiam, K.Y.; Khor, I.W. Big data and machine learning algorithms for health-care delivery. Lancet Oncol., 2019, 20(5), e262-e273.
[http://dx.doi.org/10.1016/S1470-2045(19)30149-4] [PMID: 31044724]
[13]
Kim, Y-K.; Park, S-C. Classification of psychiatric disorders. Adv. Exp. Med. Biol., 2019, 1192, 17-25.
[http://dx.doi.org/10.1007/978-981-32-9721-0_2] [PMID: 31705488]
[14]
Bzdok, D.; Meyer-Lindenberg, A. Machine learning for precision psychiatry: opportunities and challenges. Biol. Psychiatry Cogn. Neurosci. Neuroimaging, 2018, 3(3), 223-230.
[http://dx.doi.org/10.1016/j.bpsc.2017.11.007] [PMID: 29486863]
[15]
Shatte, A.B.R.; Hutchinson, D.M.; Teague, S.J. Machine learning in mental health: A scoping review of methods and applications. Psychol. Med., 2019, 49(9), 1426-1448.
[http://dx.doi.org/10.1017/S0033291719000151] [PMID: 30744717]
[16]
Wong, E.H.F.; Yocca, F.; Smith, M.A.; Lee, C-M. Challenges and opportunities for drug discovery in psychiatric disorders: the drug hunters’ perspective. Int. J. Neuropsychopharmacol., 2010, 13(9), 1269-1284.
[http://dx.doi.org/10.1017/S1461145710000866] [PMID: 20716397]
[17]
Cassidy, J.W. Applications of machine learning in drug discovery I: Target discovery and small molecule drug design; IntechOpen, 2020.
[18]
Lenze, E.J.; Nicol, G.E.; Barbour, D.L.; Kannampallil, T.; Wong, A.W.K.; Piccirillo, J.; Drysdale, A.T.; Sylvester, C.M.; Haddad, R.; Miller, J.P.; Low, C.A.; Lenze, S.N.; Freedland, K.E.; Rodebaugh, T.L. Precision clinical trials: A framework for getting to precision medicine for neurobehavioural disorders. J. Psychiatry Neurosci., 2021, 46(1), E97-E110.
[http://dx.doi.org/10.1503/jpn.200042] [PMID: 33206039]
[19]
Rajula, H.S.R.; Verlato, G.; Manchia, M.; Antonucci, N.; Fanos, V. Comparison of conventional statistical methods with machine learning in medicine: Diagnosis, drug development, and treatment. Medicina (Kaunas), 2020, 56(9), 455.
[http://dx.doi.org/10.3390/medicina56090455] [PMID: 32911665]
[20]
Xu, L.; Ru, X.; Song, R. Application of machine learning for drug-target interaction prediction. Front. Genet., 2021, 12, 680117.
[http://dx.doi.org/10.3389/fgene.2021.680117] [PMID: 34234813]
[21]
Hsu, K-C.; Wang, F-S. Model-based optimization approaches for precision medicine: A case study in presynaptic dopamine overactivity. PLoS One, 2017, 12(6), e0179575.
[http://dx.doi.org/10.1371/journal.pone.0179575] [PMID: 28614410]
[22]
Li, A.; Zalesky, A.; Yue, W.; Howes, O.; Yan, H.; Liu, Y.; Fan, L.; Whitaker, K.J.; Xu, K.; Rao, G.; Li, J.; Liu, S.; Wang, M.; Sun, Y.; Song, M.; Li, P.; Chen, J.; Chen, Y.; Wang, H.; Liu, W.; Li, Z.; Yang, Y.; Guo, H.; Wan, P.; Lv, L.; Lu, L.; Yan, J.; Song, Y.; Wang, H.; Zhang, H.; Wu, H.; Ning, Y.; Du, Y.; Cheng, Y.; Xu, J.; Xu, X.; Zhang, D.; Wang, X.; Jiang, T.; Liu, B. A neuroimaging biomarker for striatal dysfunction in schizophrenia. Nat. Med., 2020, 26(4), 558-565.
[http://dx.doi.org/10.1038/s41591-020-0793-8] [PMID: 32251404]
[23]
Xu, R.; Wang, Q. PhenoPredict: A disease phenome-wide drug repositioning approach towards schizophrenia drug discovery. J. Biomed. Inform., 2015, 56, 348-355.
[http://dx.doi.org/10.1016/j.jbi.2015.06.027] [PMID: 26151312]
[24]
Tan, X.; Jiang, X.; He, Y.; Zhong, F.; Li, X.; Xiong, Z.; Li, Z.; Liu, X.; Cui, C.; Zhao, Q.; Xie, Y.; Yang, F.; Wu, C.; Shen, J.; Zheng, M.; Wang, Z.; Jiang, H. Automated design and optimization of multitarget schizophrenia drug candidates by deep learning. Eur. J. Med. Chem., 2020, 204, 112572.
[http://dx.doi.org/10.1016/j.ejmech.2020.112572] [PMID: 32711293]
[25]
Yang, Q-X.; Wang, Y-X.; Li, F-C.; Zhang, S.; Luo, Y-C.; Li, Y.; Tang, J.; Li, B.; Chen, Y-Z.; Xue, W-W.; Zhu, F. Identification of the gene signature reflecting schizophrenia’s etiology by constructing artificial intelligence-based method of enhanced reproducibility. CNS Neurosci. Ther., 2019, 25(9), 1054-1063.
[http://dx.doi.org/10.1111/cns.13196] [PMID: 31350824]
[26]
K, Z.; Hc, S. Drug repositioning for schizophrenia and depression/anxiety disorders: a machine learning approach leveraging expression data. IEEE J. Biomed. Health Inform., 2019, 23.
[27]
Lüscher Dias, T.; Schuch, V.; Beltrão-Braga, P.C.B.; Martins-de-Souza, D.; Brentani, H.P.; Franco, G.R.; Nakaya, H.I. Drug repositioning for psychiatric and neurological disorders through a network medicine approach. Transl. Psychiatry, 2020, 10(1), 141.
[http://dx.doi.org/10.1038/s41398-020-0827-5] [PMID: 32398742]
[28]
Mizuno, M.; Iwakura, Y.; Shibuya, M.; Zheng, Y.; Eda, T.; Kato, T.; Takasu, Y.; Nawa, H. Antipsychotic potential of quinazoline ErbB1 inhibitors in a schizophrenia model established with neonatal hippocampal lesioning. J. Pharmacol. Sci., 2010, 114(3), 320-331.
[http://dx.doi.org/10.1254/jphs.10099FP] [PMID: 20962455]
[29]
MacKay, M.B.; Paylor, J.W.; Wong, J.T.F.; Winship, I.R.; Baker, G.B.; Dursun, S.M. Multidimensional connectomics and treatment-resistant schizophrenia: linking phenotypic circuits to targeted therapeutics. Front. Psychiatry, 2018, 9, 537.
[http://dx.doi.org/10.3389/fpsyt.2018.00537] [PMID: 30425662]
[30]
Koutsouleris, N.; Dwyer, D.B.; Degenhardt, F.; Maj, C.; Urquijo-Castro, M.F.; Sanfelici, R.; Popovic, D.; Oeztuerk, O.; Haas, S.S.; Weiske, J.; Ruef, A.; Kambeitz-Ilankovic, L.; Antonucci, L.A.; Neufang, S.; Schmidt-Kraepelin, C.; Ruhrmann, S.; Penzel, N.; Kambeitz, J.; Haidl, T.K.; Rosen, M.; Chisholm, K.; Riecher-Rössler, A.; Egloff, L.; Schmidt, A.; Andreou, C.; Hietala, J.; Schirmer, T.; Romer, G.; Walger, P.; Franscini, M.; Traber-Walker, N.; Schimmelmann, B.G.; Flückiger, R.; Michel, C.; Rössler, W.; Borisov, O.; Krawitz, P.M.; Heekeren, K.; Buechler, R.; Pantelis, C.; Falkai, P.; Salokangas, R.K.R.; Lencer, R.; Bertolino, A.; Borgwardt, S.; Noethen, M.; Brambilla, P.; Wood, S.J.; Upthegrove, R.; Schultze-Lutter, F.; Theodoridou, A.; Meisenzahl, E. Multimodal machine learning workflows for prediction of psychosis in patients with clinical high-risk syndromes and recent-onset depression. JAMA Psychiatry, 2021, 78, 195-209.
[http://dx.doi.org/10.1001/jamapsychiatry.2020.3604] [PMID: 33263726]
[31]
Ke, P-F.; Xiong, D-S.; Li, J-H.; Pan, Z-L.; Zhou, J.; Li, S-J.; Song, J.; Chen, X-Y.; Li, G-X.; Chen, J.; Li, X-B.; Ning, Y-P.; Wu, F-C.; Wu, K. An integrated machine learning framework for a discriminative analysis of schizophrenia using multi-biological data. Sci. Rep., 2021, 11(1), 14636.
[http://dx.doi.org/10.1038/s41598-021-94007-9] [PMID: 34282208]
[32]
Fernandes, B.S.; Karmakar, C.; Tamouza, R.; Tran, T.; Yearwood, J.; Hamdani, N.; Laouamri, H.; Richard, J-R.; Yolken, R.; Berk, M.; Venkatesh, S.; Leboyer, M. Precision psychiatry with immunological and cognitive biomarkers: A multi-domain prediction for the diagnosis of bipolar disorder or schizophrenia using machine learning. Transl. Psychiatry, 2020, 10(1), 162.
[http://dx.doi.org/10.1038/s41398-020-0836-4] [PMID: 32448868]
[33]
Aydin, O.; Unal Aydin, P.; Arslan, A. Development of neuroimaging-based biomarkers in psychiatry. Adv. Exp. Med. Biol., 2019, 1192, 159-195.
[http://dx.doi.org/10.1007/978-981-32-9721-0_9] [PMID: 31705495]
[34]
Yassin, W.; Nakatani, H.; Zhu, Y.; Kojima, M.; Owada, K.; Kuwabara, H.; Gonoi, W.; Aoki, Y.; Takao, H.; Natsubori, T.; Iwashiro, N.; Kasai, K.; Kano, Y.; Abe, O.; Yamasue, H.; Koike, S. Machine-learning classification using neuroimaging data in schizophrenia, autism, ultra-high risk and first-episode psychosis. Transl. Psychiatry, 2020, 10(1), 278.
[http://dx.doi.org/10.1038/s41398-020-00965-5] [PMID: 32801298]
[35]
Zhu, L.; Wu, X.; Xu, B.; Zhao, Z.; Yang, J.; Long, J.; Su, L. The machine learning algorithm for the diagnosis of schizophrenia on the basis of gene expression in peripheral blood. Neurosci. Lett., 2021, 745, 135596.
[http://dx.doi.org/10.1016/j.neulet.2020.135596] [PMID: 33359735]
[36]
Chen, Z.; Yan, T.; Wang, E.; Jiang, H.; Tang, Y.; Yu, X.; Zhang, J.; Liu, C. Detecting abnormal brain regions in schizophrenia using structural mri via machine learning. Comput. Intell. Neurosci., 2020, 2020, 6405930.
[http://dx.doi.org/10.1155/2020/6405930] [PMID: 32300361]
[37]
Chang, B.; Choi, Y.; Jeon, M.; Lee, J.; Han, K-M.; Kim, A.; Ham, B-J.; Kang, J. ARPNet: antidepressant response prediction network for major depressive disorder. Genes (Basel), 2019, 10(11), 10.
[http://dx.doi.org/10.3390/genes10110907] [PMID: 31703457]
[38]
Lee, Y.; Ragguett, R-M.; Mansur, R.B.; Boutilier, J.J.; Rosenblat, J.D.; Trevizol, A.; Brietzke, E.; Lin, K.; Pan, Z.; Subramaniapillai, M.; Chan, T.C.Y.; Fus, D.; Park, C.; Musial, N.; Zuckerman, H.; Chen, V.C-H.; Ho, R.; Rong, C.; McIntyre, R.S. Applications of machine learning algorithms to predict therapeutic outcomes in depression: A meta-analysis and systematic review. J. Affect. Disord., 2018, 241, 519-532.
[http://dx.doi.org/10.1016/j.jad.2018.08.073] [PMID: 30153635]
[39]
Ap, A.; D, N.; T, C.-R.; M, S.; J, B.; Ma, F.; Aj, R.; L, W.; Eb, B.; Rk, I.; Rm, W.; Wv, B. Pharmacogenomics-driven prediction of antidepressant treatment outcomes: a machine-learning approach with multi-trial replication. Clin. Pharmacol. Ther., 2019, 106.
[40]
Rajpurkar, P.; Yang, J.; Dass, N.; Vale, V.; Keller, A.S.; Irvin, J.; Taylor, Z.; Basu, S.; Ng, A.; Williams, L.M. Evaluation of a machine learning model based on pretreatment symptoms and electroencephalographic features to predict outcomes of antidepressant treatment in adults with depression: a prespecified secondary analysis of a randomized clinical trial. JAMA Netw. Open, 2020, 3(6), e206653.
[http://dx.doi.org/10.1001/jamanetworkopen.2020.6653] [PMID: 32568399]
[41]
Dipnall, J.F.; Pasco, J.A.; Berk, M.; Williams, L.J.; Dodd, S.; Jacka, F.N.; Meyer, D. Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression. PLoS One, 2016, 11(2), e0148195.
[http://dx.doi.org/10.1371/journal.pone.0148195] [PMID: 26848571]
[42]
Poletti, S.; Vai, B.; Mazza, M.G.; Zanardi, R.; Lorenzi, C.; Calesella, F.; Cazzetta, S.; Branchi, I.; Colombo, C.; Furlan, R.; Benedetti, F. A peripheral inflammatory signature discriminates bipolar from unipolar depression: A machine learning approach. Prog. Neuropsychopharmacol. Biol. Psychiatry, 2021, 105, 110136.
[http://dx.doi.org/10.1016/j.pnpbp.2020.110136] [PMID: 33045321]
[43]
Inkster, B.; Simmons, A.; Cole, J.H.; Schoof, E.; Linding, R.; Nichols, T.; Muglia, P.; Holsboer, F.; Sämann, P.G.; McGuffin, P.; Fu, C.H.Y.; Miskowiak, K.; Matthews, P.M.; Zai, G.; Nicodemus, K. Unravelling the GSK3β-related genotypic interaction network influencing hippocampal volume in recurrent major depressive disorder. Psychiatr. Genet., 2018, 28(5), 77-84.
[http://dx.doi.org/10.1097/YPG.0000000000000203] [PMID: 30080747]
[44]
Boeke, E.A.; Holmes, A.J.; Phelps, E.A. Toward robust anxiety biomarkers: a machine learning approach in a large-scale sample. Biol. Psychiatry Cogn. Neurosci. Neuroimaging, 2020, 5(8), 799-807.
[PMID: 31447329]
[45]
Malki, K.; Tosto, M.G.; Mouriño-Talín, H.; Rodríguez-Lorenzo, S.; Pain, O.; Jumhaboy, I.; Liu, T.; Parpas, P.; Newman, S.; Malykh, A.; Carboni, L.; Uher, R.; McGuffin, P.; Schalkwyk, L.C.; Bryson, K.; Herbster, M. Highly polygenic architecture of antidepressant treatment response: Comparative analysis of SSRI and NRI treatment in an animal model of depression. Am. J. Med. Genet. B. Neuropsychiatr. Genet., 2017, 174(3), 235-250.
[http://dx.doi.org/10.1002/ajmg.b.32494] [PMID: 27696737]
[46]
Perlis, R.H. A clinical risk stratification tool for predicting treatment resistance in major depressive disorder. Biol. Psychiatry, 2013, 74(1), 7-14.
[http://dx.doi.org/10.1016/j.biopsych.2012.12.007] [PMID: 23380715]
[47]
Varma, V.R.; Oommen, A.M.; Varma, S.; Casanova, R.; An, Y.; Andrews, R.M.; O’Brien, R.; Pletnikova, O.; Troncoso, J.C.; Toledo, J.; Baillie, R.; Arnold, M.; Kastenmueller, G.; Nho, K.; Doraiswamy, P.M.; Saykin, A.J.; Kaddurah-Daouk, R.; Legido-Quigley, C.; Thambisetty, M. Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study. PLoS Med., 2018, 15(1), e1002482.
[http://dx.doi.org/10.1371/journal.pmed.1002482] [PMID: 29370177]
[48]
Louros, N.; Orlando, G.; De Vleeschouwer, M.; Rousseau, F.; Schymkowitz, J. Structure-based machine-guided mapping of amyloid sequence space reveals uncharted sequence clusters with higher solubilities. Nat. Commun., 2020, 11(1), 3314.
[http://dx.doi.org/10.1038/s41467-020-17207-3] [PMID: 32620861]
[49]
Sügis, E.; Dauvillier, J.; Leontjeva, A.; Adler, P.; Hindie, V.; Moncion, T.; Collura, V.; Daudin, R.; Loe-Mie, Y.; Herault, Y.; Lambert, J-C.; Hermjakob, H.; Pupko, T.; Rain, J-C.; Xenarios, I.; Vilo, J.; Simonneau, M.; Peterson, H. HENA, heterogeneous network-based data set for Alzheimer’s disease. Sci. Data, 2019, 6(1), 151.
[http://dx.doi.org/10.1038/s41597-019-0152-0] [PMID: 31413325]
[50]
Vatansever, S.; Schlessinger, A.; Wacker, D.; Kaniskan, H.Ü.; Jin, J.; Zhou, M-M.; Zhang, B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med. Res. Rev., 2021, 41(3), 1427-1473.
[http://dx.doi.org/10.1002/med.21764] [PMID: 33295676]
[51]
Hung, T-C.; Lee, W-Y.; Chen, K-B.; Chan, Y-C.; Lee, C-C.; Chen, C.Y-C. In silico investigation of traditional Chinese medicine compounds to inhibit human histone deacetylase 2 for patients with Alzheimer’s disease. BioMed Res. Int., 2014, 2014, 769867.
[http://dx.doi.org/10.1155/2014/769867] [PMID: 25045700]
[52]
Cavas, L.; Topcam, G.; Gundogdu-Hizliates, C.; Ergun, Y. Neural network modeling of AChE inhibition by new carbazole-bearing oxazolones. Interdiscip. Sci., 2019, 11(1), 95-107.
[http://dx.doi.org/10.1007/s12539-017-0245-4] [PMID: 29236214]
[53]
Lee, J.; Kumar, S.; Lee, S-Y.; Park, S.J.; Kim, M-H. Development of predictive models for identifying potential s100a9 inhibitors based on machine learning methods. Front Chem., 2019, 7, 779.
[http://dx.doi.org/10.3389/fchem.2019.00779] [PMID: 31824919]
[54]
Miyazaki, Y.; Ono, N.; Huang, M.; Altaf-Ul-Amin, M.; Kanaya, S. Comprehensive exploration of target-specific ligands using a graph convolution neural network. Mol. Inform., 2020, 39(1-2), e1900095.
[http://dx.doi.org/10.1002/minf.201900095] [PMID: 31815371]
[55]
Fang, J.; Li, Y.; Liu, R.; Pang, X.; Li, C.; Yang, R.; He, Y.; Lian, W.; Liu, A-L.; Du, G-H. Discovery of multitarget-directed ligands against Alzheimer’s disease through systematic prediction of chemical-protein interactions. J. Chem. Inf. Model., 2015, 55(1), 149-164.
[http://dx.doi.org/10.1021/ci500574n] [PMID: 25531792]
[56]
P, A.; J, B.; T, P.; K, R. Identifying natural compounds as multi-target-directed ligands against Alzheimer’s disease: An in silico approach. J. Biomol. Struct. Dyn., 2019, 37.
[57]
Gupta, R.; Ambasta, R.K.; Kumar, P. Identification of novel class I and class IIb histone deacetylase inhibitor for Alzheimer’s disease therapeutics. Life Sci., 2020, 256, 117912.
[http://dx.doi.org/10.1016/j.lfs.2020.117912] [PMID: 32504755]
[58]
Oh, M.; Ahn, J.; Yoon, Y. A network-based classification model for deriving novel drug-disease associations and assessing their molecular actions. PLoS One, 2014, 9(10), e111668.
[http://dx.doi.org/10.1371/journal.pone.0111668] [PMID: 25356910]
[59]
Carpenter, K.; Pilozzi, A.; Huang, X. A pilot study of multi-input recurrent neural networks for drug-kinase binding prediction. Molecules, 2020, 25(15), 25.
[http://dx.doi.org/10.3390/molecules25153372] [PMID: 32722290]
[60]
Ka, C.; Ds, C.; Jt, J.; X, H. Deep Learning and Virtual Drug Screening. Future Med. Chem., 2018, 10.
[61]
Carpenter, K.A.; Huang, X. Machine learning-based virtual screening and its applications to alzheimer’s drug discovery: a review. Curr. Pharm. Des., 2018, 24(28), 3347-3358.
[http://dx.doi.org/10.2174/1381612824666180607124038] [PMID: 29879881]
[62]
Mishra, R.; Li, B. The application of artificial intelligence in the genetic study of Alzheimer’s disease. Aging Dis., 2020, 11(6), 1567-1584.
[http://dx.doi.org/10.14336/AD.2020.0312] [PMID: 33269107]
[63]
Steele, V.R.; Maurer, J.M.; Arbabshirani, M.R.; Claus, E.D.; Fink, B.C.; Rao, V.; Calhoun, V.D.; Kiehl, K.A. Machine learning of functional magnetic resonance imaging network connectivity predicts substance abuse treatment completion. Biol. Psychiatry Cogn. Neurosci. Neuroimaging, 2018, 3(2), 141-149.
[http://dx.doi.org/10.1016/j.bpsc.2017.07.003] [PMID: 29529409]
[64]
Ahn, W-Y.; Vassileva, J. Machine-learning identifies substance-specific behavioral markers for opiate and stimulant dependence. Drug Alcohol Depend., 2016, 161, 247-257.
[http://dx.doi.org/10.1016/j.drugalcdep.2016.02.008] [PMID: 26905209]
[65]
Camacho, D.M.; Collins, K.M.; Powers, R.K.; Costello, J.C.; Collins, J.J. Next-generation machine learning for biological networks. Cell, 2018, 173(7), 1581-1592.
[http://dx.doi.org/10.1016/j.cell.2018.05.015] [PMID: 29887378]
[66]
Kalinin, A.A.; Higgins, G.A.; Reamaroon, N.; Soroushmehr, S.; Allyn-Feuer, A.; Dinov, I.D.; Najarian, K.; Athey, B.D. Deep learning in pharmacogenomics: from gene regulation to patient stratification. Pharmacogenomics, 2018, 19(7), 629-650.
[http://dx.doi.org/10.2217/pgs-2018-0008] [PMID: 29697304]
[67]
Lin, E.; Lin, C-H.; Lane, H-Y. Precision Psychiatry applications with pharmacogenomics: artificial intelligence and machine learning approaches. Int. J. Mol. Sci., 2020, 21(3), 21.
[http://dx.doi.org/10.3390/ijms21030969] [PMID: 32024055]
[68]
Dazzan, P. Neuroimaging biomarkers to predict treatment response in schizophrenia: the end of 30 years of solitude? Dialogues Clin. Neurosci., 2014, 16(4), 491-503.
[http://dx.doi.org/10.31887/DCNS.2014.16.4/pdazzan] [PMID: 25733954]
[69]
Doyle, O.M.; Mehta, M.A.; Brammer, M.J. The role of machine learning in neuroimaging for drug discovery and development. Psychopharmacology (Berl.), 2015, 232(21-22), 4179-4189.
[http://dx.doi.org/10.1007/s00213-015-3968-0] [PMID: 26014110]
[70]
Tai, A.M.Y.; Albuquerque, A.; Carmona, N.E.; Subramanieapillai, M.; Cha, D.S.; Sheko, M.; Lee, Y.; Mansur, R.; McIntyre, R.S. Machine learning and big data: Implications for disease modeling and therapeutic discovery in psychiatry. Artif. Intell. Med., 2019, 99, 101704.
[http://dx.doi.org/10.1016/j.artmed.2019.101704] [PMID: 31606109]
[71]
Passos, I.C.; Ballester, P.L.; Barros, R.C.; Librenza-Garcia, D.; Mwangi, B.; Birmaher, B.; Brietzke, E.; Hajek, T.; Lopez Jaramillo, C.; Mansur, R.B.; Alda, M.; Haarman, B.C.M.; Isometsa, E.; Lam, R.W.; McIntyre, R.S.; Minuzzi, L.; Kessing, L.V.; Yatham, L.N.; Duffy, A.; Kapczinski, F. Machine learning and big data analytics in bipolar disorder: A position paper from the international society for bipolar disorders big data task force. Bipolar Disord., 2019, 21(7), 582-594.
[http://dx.doi.org/10.1111/bdi.12828] [PMID: 31465619]
[72]
Zhang, L.; Tan, J.; Han, D.; Zhu, H. From machine learning to deep learning: progress in machine intelligence for rational drug discovery. Drug Discov. Today, 2017, 22(11), 1680-1685.
[http://dx.doi.org/10.1016/j.drudis.2017.08.010] [PMID: 28881183]
[73]
Rutledge, R.B.; Chekroud, A.M.; Huys, Q.J. Machine learning and big data in psychiatry: toward clinical applications. Curr. Opin. Neurobiol., 2019, 55, 152-159.
[http://dx.doi.org/10.1016/j.conb.2019.02.006] [PMID: 30999271]
[74]
Duff, E.P.; Vennart, W.; Wise, R.G.; Howard, M.A.; Harris, R.E.; Lee, M.; Wartolowska, K.; Wanigasekera, V.; Wilson, F.J.; Whitlock, M.; Tracey, I.; Woolrich, M.W.; Smith, S.M. Learning to identify CNS drug action and efficacy using multistudy fMRI data. Sci. Transl. Med., 2015, 7(274), 274ra16.
[http://dx.doi.org/10.1126/scitranslmed.3008438] [PMID: 25673761]
[75]
Bender, A.; Cortés-Ciriano, I. Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet. Drug Discov. Today, 2021, 26(2), 511-524.
[http://dx.doi.org/10.1016/j.drudis.2020.12.009] [PMID: 33346134]
[76]
Tenenbaum, J.D.; Bhuvaneshwar, K.; Gagliardi, J.P.; Fultz Hollis, K.; Jia, P.; Ma, L.; Nagarajan, R.; Rakesh, G.; Subbian, V.; Visweswaran, S.; Zhao, Z.; Rozenblit, L. Translational bioinformatics in mental health: open access data sources and computational biomarker discovery. Brief. Bioinform., 2019, 20(3), 842-856.
[http://dx.doi.org/10.1093/bib/bbx157] [PMID: 29186302]
[77]
Fernandes, B.S.; Williams, L.M.; Steiner, J.; Leboyer, M.; Carvalho, A.F.; Berk, M. The new field of ‘precision psychiatry’. BMC Med., 2017, 15(1), 80.
[http://dx.doi.org/10.1186/s12916-017-0849-x] [PMID: 28403846]
[78]
Stoyanov, D.; Kandilarova, S.; Aryutova, K.; Paunova, R.; Todeva-Radneva, A.; Latypova, A.; Kherif, F. Multivariate analysis of structural and functional neuroimaging can inform psychiatric differential diagnosis. Diagnostics (Basel), 2020, 11(1), 19.
[http://dx.doi.org/10.3390/diagnostics11010019] [PMID: 33374207]
[79]
Kandilarova, S.; Stoyanov, D.; Stoeva, M.; Latypova, A.; Kherif, F. Functional MRI in depression-multivariate analysis of emotional task. J. Med. Biol. Eng., 2020, 40, 535-544.
[http://dx.doi.org/10.1007/s40846-020-00547-2]

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy