Novel Computational Methods for Cancer Drug Design

Weinberg, R.A. How cancer arises. Sci. Am., 1996, 275(3), 62-70.
[http://dx.doi.org/10.1038/scientificamerican0996-62] [PMID: 8701295]

Deepak, K.G.K.; Vempati, R.; Nagaraju, G.P.; Dasari, V.R.; S, N.; Rao, D.N.; Malla, R.R. Tumor microenvironment: Challenges and opportunities in targeting metastasis of triple negative breast cancer. Pharmacol. Res., 2020, 153, 104683.
[http://dx.doi.org/10.1016/j.phrs.2020.104683] [PMID: 32050092]

Cao, C.; Moult, J. GWAS and drug targets. BMC Genomics, 2014, 15, S5.
[http://dx.doi.org/10.1186/1471-2164-15-S4-S5]

Liang, B.; Ding, H.; Huang, L.; Luo, H.; Zhu, X. GWAS in cancer: Progress and challenges. Mol. Genet. Genomics, 2020, 295(3), 537-561.
[http://dx.doi.org/10.1007/s00438-020-01647-z] [PMID: 32048005]

Boža, V.; Brejová, B.; Vinař, T. DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads. PLoS One, 2017, 12(6), e0178751.
[http://dx.doi.org/10.1371/journal.pone.0178751] [PMID: 28582401]

Wei, Q.; Ji, Z.; Li, Z.; Du, J.; Wang, J.; Xu, J.; Xiang, Y.; Tiryaki, F.; Wu, S.; Zhang, Y.; Tao, C.; Xu, H. A study of deep learning approaches for medication and adverse drug event extraction from clinical text. J. Am. Med. Inform. Assoc., 2020, 27(1), 13-21.
[http://dx.doi.org/10.1093/jamia/ocz063] [PMID: 31135882]

Gorostiola González, M.; Janssen, A.P.A.; IJzerman, A.P.; Heitman, L.H.; van Westen, G.J.P. Oncological drug discovery: AI meets structure-based computational research. Drug Discov. Today, 2022, 27(6), 1661-1670.
[http://dx.doi.org/10.1016/j.drudis.2022.03.005] [PMID: 35301149]

Rui Chang; Shoemaker, R.; Wei Wang A novel knowledge-driven systems biology approach for phenotype prediction upon genetic intervention. IEEE/ACM Trans. Comput. Biol. Bioinformatics, 2011, 8(5), 1170-1182.
[http://dx.doi.org/10.1109/TCBB.2011.18] [PMID: 21282866]

Lu, Y.; Bi, J.; Li, F.; Wang, G.; Zhu, J.; Jin, J.; Liu, Y. Differential gene analysis of trastuzumab in breast cancer based on network pharmacology and medical images. Front. Physiol., 2022, 13, 942049.
[http://dx.doi.org/10.3389/fphys.2022.942049] [PMID: 35874525]

Li, S.; Wu, S.; Wang, L.; Li, F.; Jiang, H.; Bai, F. Recent advances in predicting protein–protein interactions with the aid of artificial intelligence algorithms. Curr. Opin. Struct. Biol., 2022, 73, 102344.
[http://dx.doi.org/10.1016/j.sbi.2022.102344] [PMID: 35219216]

Tunyasuvunakool, K.; Adler, J.; Wu, Z.; Green, T.; Zielinski, M.; Žídek, A.; Bridgland, A.; Cowie, A.; Meyer, C.; Laydon, A.; Velankar, S.; Kleywegt, G.J.; Bateman, A.; Evans, R.; Pritzel, A.; Figurnov, M.; Ronneberger, O.; Bates, R.; Kohl, S.A.A.; Potapenko, A.; Ballard, A.J.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Clancy, E.; Reiman, D.; Petersen, S.; Senior, A.W.; Kavukcuoglu, K.; Birney, E.; Kohli, P.; Jumper, J.; Hassabis, D. Highly accurate protein structure prediction for the human proteome. Nature, 2021, 596(7873), 590-596.
[http://dx.doi.org/10.1038/s41586-021-03828-1] [PMID: 34293799]

Polanski, J. Unsupervised learning in drug design from self-organization to deep chemistry. Int. J. Mol. Sci., 2022, 23(5), 2797.
[http://dx.doi.org/10.3390/ijms23052797] [PMID: 35269939]

Nag, S.; Baidya, A.T.K.; Mandal, A.; Mathew, A.T.; Das, B.; Devi, B.; Kumar, R. Deep learning tools for advancing drug discovery and development. 3 Biotech, 2022, 12(5), 110.2022,

Liu, M.; Shen, X.; Pan, W. Deep reinforcement learning for personalized treatment recommendation. Stat. Med., 2022, 41(20), 4034-4056.
[http://dx.doi.org/10.1002/sim.9491] [PMID: 35716038]

Kabir, A.; Muth, A. Polypharmacology: The science of multi-targeting molecules. Pharmacol. Res., 2022, 176, 106055.
[http://dx.doi.org/10.1016/j.phrs.2021.106055] [PMID: 34990865]

Zhang, T.; Zhang, L.; Payne, P.R.O.; Li, F. Synergistic drug combination prediction by integrating multiomics data in deep learning models. Methods Mol. Biol., 2021, 2194, 223-238.
[http://dx.doi.org/10.1007/978-1-0716-0849-4_12] [PMID: 32926369]

Garofalo, M.; Grazioso, G.; Cavalli, A.; Sgrignani, J. How computational chemistry and drug delivery techniques can support the development of new anticancer drugs. Molecules, 2020, 25(7), 1756.
[http://dx.doi.org/10.3390/molecules25071756] [PMID: 32290224]

Bannigan, P.; Aldeghi, M.; Bao, Z.; Häse, F.; Aspuru-Guzik, A.; Allen, C. Machine learning directed drug formulation development. Adv. Drug Deliv. Rev., 2021, 175, 113806.
[http://dx.doi.org/10.1016/j.addr.2021.05.016] [PMID: 34019959]

Pantziarka, P.; Verbaanderd, C.; Huys, I.; Bouche, G.; Meheus, L. Repurposing drugs in oncology: From candidate selection to clinical adoption. Semin. Cancer Biol., 2021, 68, 186-191.
[http://dx.doi.org/10.1016/j.semcancer.2020.01.008] [PMID: 31982510]

Alaimo, S.; Pulvirenti, A. Network-based drug repositioning: Approaches, resources, and research directions. Methods Mol. Biol., 2019, 1903, 97-113.
[http://dx.doi.org/10.1007/978-1-4939-8955-3_6] [PMID: 30547438]

Prada-Gracia, D.; Huerta-Yépez, S.; Moreno-Vargas, L.M. Application of computational methods for anticancer drug discovery, design, and optimization. Bol. Méd. Hosp. Infant. México, 2016, 73(6), 411-423.
[http://dx.doi.org/10.1016/j.bmhimx.2016.10.006] [PMID: 29421286]

Ren, N.; Yu, L.; Qian, L.; Ye, G.; Zhu, Z.; Yu, J.; Sun, L.; Zhang, L. Exploring the pharmacological mechanism of the effective chinese medicines against gynecological cancer based on meta-analysis combined with network pharmacology analysis. Front. Oncol., 2022, 12, 817772.
[http://dx.doi.org/10.3389/fonc.2022.817772] [PMID: 35875080]

Eisenberg, M.C.; Jain, H.V. A confidence building exercise in data and identifiability: Modeling cancer chemotherapy as a case study. J. Theor. Biol., 2017, 431, 63-78.
[http://dx.doi.org/10.1016/j.jtbi.2017.07.018] [PMID: 28733187]

Gorgulla, C.; Jayaraj, A.; Fackeldey, K.; Arthanari, H. Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches. Curr. Opin. Chem. Biol., 2022, 69, 102156.
[http://dx.doi.org/10.1016/j.cbpa.2022.102156] [PMID: 35576813]

Shreve, J.T.; Khanani, S.A.; Haddad, T.C. Artificial intelligence in oncology: Current capabilities, future opportunities, and ethical considerations. Am. Soc. Clin. Oncol. Educ. Book, 2022, 42(42), 842-851.
[http://dx.doi.org/10.1200/EDBK_350652] [PMID: 35687826]

Cui, W.; Aouidate, A.; Wang, S.; Yu, Q.; Li, Y.; Yuan, S. Discovering anti-cancer drugs via computational methods. Front. Pharmacol., 2020, 11, 733.
[http://dx.doi.org/10.3389/fphar.2020.00733] [PMID: 32508653]

Brady, R.; Enderling, H. Mathematical Models of Cancer: When to predict novel therapies, and when not to. Bull. Math. Biol., 2019, 81(10), 3722-3731.
[http://dx.doi.org/10.1007/s11538-019-00640-x] [PMID: 31338741]

Rahman, M.M.; Islam, M.R.; Rahman, F.; Rahaman, M.S.; Khan, M.S.; Abrar, S.; Ray, T.K.; Uddin, M.B.; Kali, M.S.K.; Dua, K.; Kamal, M.A.; Chellappan, D.K. Emerging promise of computational techniques in anti-cancer research: At a glance. Bioengineering, 2022, 9(8), 335.
[http://dx.doi.org/10.3390/bioengineering9080335] [PMID: 35892749]

Gupta, R.; Srivastava, D.; Sahu, M.; Tiwari, S.; Ambasta, R.K.; Kumar, P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol. Divers., 2021, 25(3), 1315-1360.
[http://dx.doi.org/10.1007/s11030-021-10217-3] [PMID: 33844136]

Bossé, Y.; Amos, C.I. A decade of GWAS results in lung cancer. Cancer Epidemiol. Biomarkers Prev., 2018, 27(4), 363-379.
[http://dx.doi.org/10.1158/1055-9965.EPI-16-0794] [PMID: 28615365]

Fachal, L.; Dunning, A.M. From candidate gene studies to GWAS and post-GWAS analyses in breast cancer. Curr. Opin. Genet. Dev., 2015, 30, 32-41.
[http://dx.doi.org/10.1016/j.gde.2015.01.004] [PMID: 25727315]

Dezső, Z.; Ceccarelli, M. Machine learning prediction of oncology drug targets based on protein and network properties. BMC Bioinf., 2020, 21(1), 104.
[http://dx.doi.org/10.1186/s12859-020-3442-9] [PMID: 32171238]

Yeh, S.H.; Yeh, H.Y.; Soo, V.W. A network flow approach to predict drug targets from microarray data, disease genes and interactome network - case study on prostate cancer. J. Clin. Bioinf., 2012, 2(1), 1.
[http://dx.doi.org/10.1186/2043-9113-2-1] [PMID: 22239822]

Ciriello, G.; Cerami, E.; Sander, C.; Schultz, N. Mutual exclusivity analysis identifies oncogenic network modules. Genome Res., 2012, 22(2), 398-406.
[http://dx.doi.org/10.1101/gr.125567.111] [PMID: 21908773]

Singh, R.; Devkota, K.; Sledzieski, S.; Berger, B.; Cowen, L. Topsy-Turvy: Integrating a global view into sequence-based PPI prediction. Bioinformatics, 2022, 38(Suppl. 1), i264-i272.
[http://dx.doi.org/10.1093/bioinformatics/btac258] [PMID: 35758793]

Ghedira, K.; Hamdi, Y.; El Béji, A.; Othman, H. An integrative computational approach for the prediction of human-Plasmodium protein-protein interactions. BioMed Res. Int., 2020, 2020, 1-11.
[http://dx.doi.org/10.1155/2020/2082540] [PMID: 33426052]

Kanitkar, T.R.; Sen, N.; Nair, S.; Soni, N.; Amritkar, K.; Ramtirtha, Y.; Madhusudhan, M.S. Methods for molecular modelling of protein complexes. Methods Mol. Biol., 2021, 2305, 53-80.
[http://dx.doi.org/10.1007/978-1-0716-1406-8_3] [PMID: 33950384]

Hu, L.; Wang, X.; Huang, Y.A.; Hu, P.; You, Z.H. A survey on computational models for predicting protein–protein interactions. Brief. Bioinform., 2021, 22(5), bbab036.
[http://dx.doi.org/10.1093/bib/bbab036] [PMID: 33693513]

Yin, R.; Feng, B.Y.; Varshney, A.; Pierce, B.G. Benchmarking ALPHAFOLD for protein complex modeling reveals accuracy determinants. Protein Sci., 2022, 31(8), e4379.
[http://dx.doi.org/10.1002/pro.4379] [PMID: 35900023]

Jumper, J.; Evans, R.; Pritzel, A.; Green, T.; Figurnov, M.; Ronneberger, O.; Tunyasuvunakool, K.; Bates, R.; Žídek, A.; Potapenko, A.; Bridgland, A.; Meyer, C.; Kohl, S.A.A.; Ballard, A.J.; Cowie, A.; Romera-Paredes, B.; Nikolov, S.; Jain, R.; Adler, J.; Back, T.; Petersen, S.; Reiman, D.; Clancy, E.; Zielinski, M.; Steinegger, M.; Pacholska, M.; Berghammer, T.; Bodenstein, S.; Silver, D.; Vinyals, O.; Senior, A.W.; Kavukcuoglu, K.; Kohli, P.; Hassabis, D. Highly accurate protein structure prediction with AlphaFold. Nature, 2021, 596(7873), 583-589.
[http://dx.doi.org/10.1038/s41586-021-03819-2] [PMID: 34265844]

Wang, S.; Lin, H.; Huang, Z.; He, Y.; Deng, X.; Xu, Y.; Pei, J.; Lai, L. CavitySpace: A database of potential ligand binding sites in the human proteome. Biomolecules, 2022, 12(7), 967.
[http://dx.doi.org/10.3390/biom12070967] [PMID: 35883523]

Macari, G.; Toti, D.; Polticelli, F. Computational methods and tools for binding site recognition between proteins and small molecules: from classical geometrical approaches to modern machine learning strategies. J. Comput. Aided Mol. Des., 2019, 33(10), 887-903.
[http://dx.doi.org/10.1007/s10822-019-00235-7] [PMID: 31628659]

Dhakal, A.; McKay, C.; Tanner, J.J.; Cheng, J. Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions. Brief. Bioinform., 2022, 23(1), bbab476.
[http://dx.doi.org/10.1093/bib/bbab476] [PMID: 34849575]

Pinzi, L.; Rastelli, G. Molecular docking: Shifting paradigms in drug discovery. Int. J. Mol. Sci., 2019, 20(18), 4331.
[http://dx.doi.org/10.3390/ijms20184331] [PMID: 31487867]

Trott, O.; Olson, A.J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461.
[PMID: 19499576]

Eberhardt, J.; Santos-Martins, D.; Tillack, A.F.; Forli, S. AutoDock Vina 1.2.0: New docking methods, expanded force field, and python bindings. J. Chem. Inf. Model., 2021, 61(8), 3891-3898.
[http://dx.doi.org/10.1021/acs.jcim.1c00203] [PMID: 34278794]

Wang, C.; Zhang, Y. Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest. J. Comput. Chem., 2017, 38(3), 169-177.
[http://dx.doi.org/10.1002/jcc.24667] [PMID: 27859414]

Zhang, B.; Li, H.; Yu, K.; Jin, Z. Molecular docking-based computational platform for high-throughput virtual screening. CCF Transactions on High Performance Computing, 2022, 4(1), 63-74.
[http://dx.doi.org/10.1007/s42514-021-00086-5] [PMID: 35039800]

Aziz, M.; Ejaz, S.A.; Zargar, S.; Akhtar, N.; Aborode, A.T.; A Wani, T.; Batiha, G.E.; Siddique, F.; Alqarni, M.; Akintola, A.A. Deep learning and structure-based virtual screening for drug discovery against NEK7: A novel target for the treatment of cancer. Molecules, 2022, 27(13), 4098.
[http://dx.doi.org/10.3390/molecules27134098] [PMID: 35807344]

Kerrigan, J.E. Molecular dynamics simulations in drug design. Methods Mol. Biol., 2013, 993, 95-113.
[http://dx.doi.org/10.1007/978-1-62703-342-8_7] [PMID: 23568466]

Cheng, Y.; Gong, Y.; Liu, Y.; Song, B.; Zou, Q. Molecular design in drug discovery: A comprehensive review of deep generative models. Brief. Bioinf., 2021, 22(6), bbab344.
[http://dx.doi.org/10.1093/bib/bbab344] [PMID: 34415297]

Segler, M.H.S.; Kogej, T.; Tyrchan, C.; Waller, M.P. Generating focused molecule libraries for drug discovery with recurrent neural networks. ACS Cent. Sci., 2018, 4(1), 120-131.
[http://dx.doi.org/10.1021/acscentsci.7b00512] [PMID: 29392184]

Joshi, R.P.; Kumar, N. Artificial intelligence for autonomous molecular design: A perspective. Molecules, 2021, 26(22), 6761.
[http://dx.doi.org/10.3390/molecules26226761] [PMID: 34833853]

Lim, J.; Hwang, S.Y.; Moon, S.; Kim, S.; Kim, W.Y. Scaffold-based molecular design with a graph generative model. Chem. Sci., 2020, 11(4), 1153-1164.
[http://dx.doi.org/10.1039/C9SC04503A] [PMID: 34084372]

Hong, S.H.; Ryu, S.; Lim, J.; Kim, W.Y. Molecular generative model based on an adversarially regularized autoencoder. J. Chem. Inf. Model., 2020, 60(1), 29-36.
[http://dx.doi.org/10.1021/acs.jcim.9b00694] [PMID: 31820983]

Putin, E.; Asadulaev, A.; Ivanenkov, Y.; Aladinskiy, V.; Sanchez-Lengeling, B.; Aspuru-Guzik, A.; Zhavoronkov, A. Reinforced adversarial neural computer for de novo molecular design. J. Chem. Inf. Model., 2018, 58(6), 1194-1204.
[http://dx.doi.org/10.1021/acs.jcim.7b00690] [PMID: 29762023]

Flam-Shepherd, D.; Zhu, K.; Aspuru-Guzik, A. Language models can learn complex molecular distributions. Nat. Commun., 2022, 13(1), 3293.
[http://dx.doi.org/10.1038/s41467-022-30839-x] [PMID: 35672310]

He, J.; You, H.; Sandström, E.; Nittinger, E.; Bjerrum, E.J.; Tyrchan, C.; Czechtizky, W.; Engkvist, O. Molecular optimization by capturing chemist’s intuition using deep neural networks. J. Cheminform., 2021, 13(1), 26.
[http://dx.doi.org/10.1186/s13321-021-00497-0] [PMID: 33743817]

Wang, M.; Hsieh, C.Y.; Wang, J.; Wang, D.; Weng, G.; Shen, C.; Yao, X.; Bing, Z.; Li, H.; Cao, D.; Hou, T. RELATION: A deep generative model for structure-based de novo drug design. J. Med. Chem., 2022, 65(13), 9478-9492.
[http://dx.doi.org/10.1021/acs.jmedchem.2c00732] [PMID: 35713420]

Li, Y.; Hu, J.; Wang, Y.; Zhou, J.; Zhang, L.; Liu, Z. DeepScaffold: A comprehensive tool for scaffold-based de novo drug discovery using deep learning. J. Chem. Inf. Model., 2020, 60(1), 77-91.
[http://dx.doi.org/10.1021/acs.jcim.9b00727] [PMID: 31809029]

Zheng, S.; Lei, Z.; Ai, H.; Chen, H.; Deng, D.; Yang, Y. Deep scaffold hopping with multimodal transformer neural networks. J. Cheminform., 2021, 13(1), 87.
[http://dx.doi.org/10.1186/s13321-021-00565-5] [PMID: 34774103]

Zhavoronkov, A.; Ivanenkov, Y.A.; Aliper, A.; Veselov, M.S.; Aladinskiy, V.A.; Aladinskaya, A.V.; Terentiev, V.A.; Polykovskiy, D.A.; Kuznetsov, M.D.; Asadulaev, A.; Volkov, Y.; Zholus, A.; Shayakhmetov, R.R.; Zhebrak, A.; Minaeva, L.I.; Zagribelnyy, B.A.; Lee, L.H.; Soll, R.; Madge, D.; Xing, L.; Guo, T.; Aspuru-Guzik, A. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol., 2019, 37(9), 1038-1040.
[http://dx.doi.org/10.1038/s41587-019-0224-x] [PMID: 31477924]

Ongusaha, P.P.; Kim, J.I.; Fang, L.; Wong, T.W.; Yancopoulos, G.D.; Aaronson, S.A.; Lee, S.W. p53 induction and activation of DDR1 kinase counteract p53-mediated apoptosis and influence p53 regulation through a positive feedback loop. EMBO J., 2003, 22(6), 1289-1301.
[http://dx.doi.org/10.1093/emboj/cdg129] [PMID: 12628922]

Pereira, T.; Abbasi, M.; Oliveira, R.I.; Guedes, R.A.; Salvador, J.A.R.; Arrais, J.P. Deep generative model for therapeutic targets using transcriptomic disease-associated data-USP7 case study. Brief. Bioinform., 2022, 23(4), bbac270.
[http://dx.doi.org/10.1093/bib/bbac270] [PMID: 35789255]

Wang, J.; Chu, Y.; Mao, J.; Jeon, H.N.; Jin, H.; Zeb, A.; Jang, Y.; Cho, K.H.; Song, T.; No, K.T. De novo molecular design with deep molecular generative models for PPI inhibitors. Brief. Bioinform., 2022, 23(4), bbac285.
[http://dx.doi.org/10.1093/bib/bbac285] [PMID: 35830870]

Khan, M.F.; Verma, G.; Akhtar, W.; Shaquiquzzaman, M.; Akhter, M.; Rizvi, M.A.; Alam, M.M. Pharmacophore modeling, 3D-QSAR, docking study and ADME prediction of acyl 1,3,4-thiadiazole amides and sulfonamides as antitubulin agents. Arab. J. Chem., 2019, 12(8), 5000-5018.
[http://dx.doi.org/10.1016/j.arabjc.2016.11.004]

Elkaeed, E.B.; Yousef, R.G.; Elkady, H.; Gobaara, I.M.M.; Alsfouk, B.A.; Husein, D.Z.; Ibrahim, I.M.; Metwaly, A.M.; Eissa, I.H. Design, synthesis, docking, DFT, MD simulation studies of a new nicotinamide-based derivative: In vitro anticancer and VEGFR-2 inhibitory effects. Molecules, 2022, 27(14), 4606.
[http://dx.doi.org/10.3390/molecules27144606] [PMID: 35889478]

Iwaloye, O.; Elekofehinti, O.O.; Kikiowo, B.; Oluwarotimi, E.A.; Fadipe, T.M. Machine learning-based virtual screening strategy revealssome natural compounds as potential PAK4 inhibitors in triple negative breast cancer. Curr. Proteomics, 2021, 18(5), 753-769.
[http://dx.doi.org/10.2174/1570164618999201223092209]

Méndez-Lucio, O.; Baillif, B.; Clevert, D.A.; Rouquié, D.; Wichard, J. De novo generation of hit-like molecules from gene expression signatures using artificial intelligence. Nat. Commun., 2020, 11(1), 10.
[http://dx.doi.org/10.1038/s41467-019-13807-w] [PMID: 31900408]

Sharma, V.; Wakode, S.; Kumar, H. Structure-and ligand-based drug design: concepts, approaches, and challenges; Chemoinformatics and Bioinformatics in the Pharmaceutical Sciences, 2021, pp. 27-53.
[http://dx.doi.org/10.1016/B978-0-12-821748-1.00004-X]

Loganathan, L.; Muthusamy, K. Current Scenario in structure and ligand-based drug design on anti-colon cancer drugs. Curr. Pharm. Des., 2019, 24(32), 3829-3841.
[http://dx.doi.org/10.2174/1381612824666181114114513] [PMID: 30426891]

Sanyal, S.; Amin, S.A.; Adhikari, N.; Jha, T. Ligand-based design of anticancer MMP2 inhibitors: A review. Future Med. Chem., 2021, 13(22), 1987-2013.
[http://dx.doi.org/10.4155/fmc-2021-0262] [PMID: 34634916]

Hussin, S.K.; Omar, Y.M.; Abdelmageid, S.M.; Marie, M.I. Traditional machine learning and big data analytics in virtual screening: A comparative study. Int. J. Adv. Comput. Res., 2020, 10(47), 72-88.
[http://dx.doi.org/10.19101/IJACR.2019.940150]

Carracedo-Reboredo, P.; Liñares-Blanco, J.; Rodríguez-Fernández, N.; Cedrón, F.; Novoa, F.J.; Carballal, A.; Maojo, V.; Pazos, A.; Fernandez-Lozano, C. A review on machine learning approaches and trends in drug discovery. Comput. Struct. Biotechnol. J., 2021, 19, 4538-4558.
[http://dx.doi.org/10.1016/j.csbj.2021.08.011] [PMID: 34471498]

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), 3098-3104.
[http://dx.doi.org/10.1021/acs.molpharmaceut.7b00346] [PMID: 28703000]

Tsou, L.K.; Yeh, S.H.; Ueng, S.H.; Chang, C.P.; Song, J.S.; Wu, M.H.; Chang, H.F.; Chen, S.R.; Shih, C.; Chen, C.T.; Ke, Y.Y. Comparative study between deep learning and QSAR classifications for TNBC inhibitors and novel GPCR agonist discovery. Sci. Rep., 2020, 10(1), 16771.
[http://dx.doi.org/10.1038/s41598-020-73681-1] [PMID: 33033310]

Daoud, N.E.H.; Borah, P.; Deb, P.K.; Venugopala, K.N.; Hourani, W.; Alzweiri, M.; Bardaweel, S.K.; Tiwari, V. ADMET profiling in drug discovery and development: Perspectives of in silico, in vitro and integrated approaches. Curr. Drug Metab., 2021, 22(7), 503-522.
[http://dx.doi.org/10.2174/1389200222666210705122913] [PMID: 34225615]

Keyvanpour, M.R.; Shirzad, M.B. An analysis of QSAR research based on machine learning concepts. Curr. Drug Discov. Technol., 2021, 18(1), 17-30.
[http://dx.doi.org/10.2174/1570163817666200316104404] [PMID: 32178612]

Zhang, W.; Xue, Z.; Li, Z.; Yin, H. DCE-DForest: A deep forest model for the prediction of anticancer drug combination effects. Comput. Math. Methods Med., 2022, 2022, 1-5.
[http://dx.doi.org/10.1155/2022/8693746] [PMID: 35720022]

Celebi, R.; Bear Don’t Walk, O., IV; Movva, R.; Alpsoy, S.; Dumontier, M. In-silico prediction of synergistic anti- cancer drug combinations using multi-omics data. Sci. Rep., 2019, 9(1), 8949.
[http://dx.doi.org/10.1038/s41598-019-45236-6] [PMID: 31222109]

Liu, H.; Zhang, W.; Nie, L.; Ding, X.; Luo, J.; Zou, L. Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous network. BMC Bioinformatics, 2019, 20(1), 645.
[http://dx.doi.org/10.1186/s12859-019-3288-1] [PMID: 31818267]

Alaparthi, S.; Mishra, M. Bidirectional Encoder Representations from Transformers (BERT): A sentiment analysis odyssey. 2007.011272020.

Wu, Z.; Jiang, D.; Wang, J.; Zhang, X.; Du, H.; Pan, L.; Hsieh, C.Y.; Cao, D.; Hou, T. Knowledge-based BERT: A method to extract molecular features like computational chemists. Brief. Bioinform., 2022, 23(3), bbac131.
[http://dx.doi.org/10.1093/bib/bbac131] [PMID: 35438145]

Khan, D.; Shedole, S. Leveraging deep learning techniques and integrated omics data for tailored treatment of breast cancer. J. Pers. Med., 2022, 12(5), 674.
[http://dx.doi.org/10.3390/jpm12050674] [PMID: 35629097]

Xia, F.; Shukla, M.; Brettin, T.; Garcia-Cardona, C.; Cohn, J.; Allen, J.E.; Maslov, S.; Holbeck, S.L.; Doroshow, J.H.; Evrard, Y.A.; Stahlberg, E.A.; Stevens, R.L. Predicting tumor cell line response to drug pairs with deep learning. BMC Bioinf., 2018, 19(Suppl. 18), 486.
[http://dx.doi.org/10.1186/s12859-018-2509-3] [PMID: 30577754]

Wang, J.; Liu, X.; Shen, S.; Deng, L.; Liu, H. DeepDDS: deep graph neural network with attention mechanism to predict synergistic drug combinations. Brief. Bioinf., 2022, 23(1), bbab390.
[http://dx.doi.org/10.1093/bib/bbab390] [PMID: 34571537]

Liu, Q.; Xie, L. TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations. PLoS Comput. Biol., 2021, 17(2), e1008653.
[http://dx.doi.org/10.1371/journal.pcbi.1008653] [PMID: 33577560]

Schmucker, R.; Farina, G.; Faeder, J.; Fröhlich, F.; Saglam, A.S.; Sandholm, T. Combination treatment optimization using a pan-cancer pathway model. PLoS Comput. Biol., 2021, 17(12), e1009689.
[http://dx.doi.org/10.1371/journal.pcbi.1009689] [PMID: 34962919]

Enriquez-Navas, P.M.; Kam, Y.; Das, T.; Hassan, S.; Silva, A.; Foroutan, P.; Ruiz, E.; Martinez, G.; Minton, S.; Gillies, R.J.; Gatenby, R.A. Exploiting evolutionary principles to prolong tumor control in preclinical models of breast cancer. Sci. Transl. Med., 2016, 8(327), 327ra24.
[http://dx.doi.org/10.1126/scitranslmed.aad7842] [PMID: 26912903]

Zhang, J.; Cunningham, J.J.; Brown, J.S.; Gatenby, R.A. Integrating evolutionary dynamics into treatment of metastatic castrate-resistant prostate cancer. Nat. Commun., 2017, 8(1), 1816.
[http://dx.doi.org/10.1038/s41467-017-01968-5] [PMID: 29180633]

Galati, S.; Di Stefano, M.; Martinelli, E.; Poli, G.; Tuccinardi, T. Recent advances in in silico target fishing. Molecules, 2021, 26(17), 5124.
[http://dx.doi.org/10.3390/molecules26175124] [PMID: 34500568]

Mohanasundaram, N.; Sekhar, T. Computational studies of molecular targets regarding the adverse effects of isoniazid drug for tuberculosis. Curr. Pharmacogenomics Person. Med., 2019, 16(3), 210-218.
[http://dx.doi.org/10.2174/1875692116666181108145230]

Metzger, M.H.; Gadji, A.; Haj Salah, N.; Kane, W.; Boue, F. Deep learning methods for detecting side effects of cancer chemotherapies reported in a remote monitoring web application. Stud. Health Technol. Inform., 2022, 294, 880-881.
[http://dx.doi.org/10.3233/SHTI220616] [PMID: 35612235]

Blaschke, T.; Bajorath, J. Fine-tuning of a generative neural network for designing multi-target compounds. J. Comput. Aided Mol. Des., 2022, 36(5), 363-371.
[http://dx.doi.org/10.1007/s10822-021-00392-8] [PMID: 34046745]

Fan, Y.W.; Liu, W.H.; Chen, Y.T.; Hsu, Y.C.; Pathak, N.; Huang, Y.W.; Yang, J.M. Exploring kinase family inhibitors and their moiety preferences using deep SHapley additive exPlanations. BMC Bioinf., 2022, 23(Suppl. 4), 242.
[http://dx.doi.org/10.1186/s12859-022-04760-5] [PMID: 35725381]

Talluri, S. Molecular docking and virtual screening based prediction of drugs for COVID-19. Comb. Chem. High Throughput Screen., 2021, 24(5), 716-728.
[http://dx.doi.org/10.2174/13862073MTA5sMTEzz] [PMID: 32798373]

Lotfi Shahreza, M.; Ghadiri, N.; Mousavi, S.R.; Varshosaz, J.; Green, J.R. A review of network-based approaches to drug repositioning. Brief. Bioinf., 2018, 19(5), 878-892.
[http://dx.doi.org/10.1093/bib/bbx017] [PMID: 28334136]

Armando, R.G.; Mengual Gómez, D.L.; Gomez, D.E. New drugs are not enough-drug repositioning in oncology: An update. Int. J. Oncol., 2020, 56(3), 651-684.
[http://dx.doi.org/10.3892/ijo.2020.4966] [PMID: 32124955]

Marshall, G.R. Computer-aided drug design. Annu. Rev. Pharmacol. Toxicol., 1987, 27(1), 193-213.
[http://dx.doi.org/10.1146/annurev.pa.27.040187.001205] [PMID: 3555315]

Emig, D.; Ivliev, A.; Pustovalova, O.; Lancashire, L.; Bureeva, S.; Nikolsky, Y.; Bessarabova, M. Drug target prediction and repositioning using an integrated network-based approach. PLoS One, 2013, 8(4), e60618.
[http://dx.doi.org/10.1371/journal.pone.0060618] [PMID: 23593264]

Brown, A.S.; Kong, S.W.; Kohane, I.S.; Patel, C.J. ksRepo: A generalized platform for computational drug repositioning. BMC Bioinf., 2016, 17(1), 78.
[http://dx.doi.org/10.1186/s12859-016-0931-y] [PMID: 26860211]

Imoto, S.; Tamada, Y.; Savoie, C.J.; Miyano, S. Analysis of gene networks for drug target discovery and validation. Methods Mol. Biol., 2007, 360, 33-56.
[PMID: 17172724]

Chen, H.R.; Sherr, D.H.; Hu, Z.; DeLisi, C. A network based approach to drug repositioning identifies plausible candidates for breast cancer and prostate cancer. BMC Med. Genomics, 2016, 9(1), 51.
[http://dx.doi.org/10.1186/s12920-016-0212-7] [PMID: 27475327]

Zhao, Y.; Liu, Y.; Bai, H. Integrating LINCS data to evaluate cancer transcriptome modifying potential of small- molecule compounds for drug repositioning. Comb. Chem. High Throughput Screen., 2021, 24(9), 1340-1350.
[http://dx.doi.org/10.2174/1386207323666201027120149] [PMID: 33109034]

Yang, H.T.; Ju, J.H.; Wong, Y.T.; Shmulevich, I.; Chiang, J.H. Literature-based discovery of new candidates for drug repurposing. Brief. Bioinform., 2017, 18(3), 488-497.
[PMID: 27113728]

Iorio, F.; Bosotti, R.; Scacheri, E.; Belcastro, V.; Mithbaokar, P.; Ferriero, R.; Murino, L.; Tagliaferri, R.; Brunetti-Pierri, N.; Isacchi, A.; di Bernardo, D. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc. Natl. Acad. Sci. USA, 2010, 107(33), 14621-14626.
[http://dx.doi.org/10.1073/pnas.1000138107] [PMID: 20679242]

Folger, O.; Jerby, L.; Frezza, C.; Gottlieb, E.; Ruppin, E.; Shlomi, T. Predicting selective drug targets in cancer through metabolic networks. Mol. Syst. Biol., 2011, 7(1), 501.
[http://dx.doi.org/10.1038/msb.2011.35] [PMID: 21694718]

Wang, W.; Yang, S.; Zhang, X.; Li, J. Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics, 2014, 30(20), 2923-2930.
[http://dx.doi.org/10.1093/bioinformatics/btu403] [PMID: 24974205]

Lee, H.; Kang, S.; Kim, W. Drug repositioning for cancer therapy based on large-scale drug-induced transcriptional signatures. PLoS One, 2016, 11(3), e0150460.
[http://dx.doi.org/10.1371/journal.pone.0150460] [PMID: 26954019]

Tao, C.; Sun, J.; Zheng, W.J.; Chen, J.; Xu, H. Colorectal cancer drug target prediction using ontology-based inference and network analysis. Database, 2015, 2015
[http://dx.doi.org/10.1093/database/bav015]

Qin, Y.; Chen, M.; Wang, H.; Zheng, X. A network flow-based method to predict anticancer drug sensitivity. PLoS One, 2015, 10(5), e0127380.
[http://dx.doi.org/10.1371/journal.pone.0127380] [PMID: 25992881]

Ko, Y.K.; Gim, J.A. New drug development and clinical trial design by applying genomic information management. Pharmaceutics, 2022, 14(8), 1539.
[http://dx.doi.org/10.3390/pharmaceutics14081539] [PMID: 35893795]

Deamer, D.; Akeson, M.; Branton, D. Three decades of nanopore sequencing. Nat. Biotechnol., 2016, 34(5), 518-524.
[http://dx.doi.org/10.1038/nbt.3423] [PMID: 27153285]

Norris, A.L.; Workman, R.E.; Fan, Y.; Eshleman, J.R.; Timp, W. Nanopore sequencing detects structural variants in cancer. Cancer Biol. Ther., 2016, 17(3), 246-253.
[http://dx.doi.org/10.1080/15384047.2016.1139236] [PMID: 26787508]

Rang, F.J.; Kloosterman, W.P.; de Ridder, J. From squiggle to basepair: Computational approaches for improving nanopore sequencing read accuracy. Genome Biol., 2018, 19(1), 90.
[http://dx.doi.org/10.1186/s13059-018-1462-9] [PMID: 30005597]

Low, Z.Y.; Farouk, I.A.; Lal, S.K. Drug Repositioning: New approaches and future prospects for life-debilitating diseases and the COVID-19 pandemic outbreak. Viruses, 2020, 12(9), 1058.
[http://dx.doi.org/10.3390/v12091058] [PMID: 32972027]

Isik, Z.; Baldow, C.; Cannistraci, C.V.; Schroeder, M. Drug target prioritization by perturbed gene expression and network information. Sci. Rep., 2015, 5(1), 17417.
[http://dx.doi.org/10.1038/srep17417] [PMID: 26615774]

Kotsias, P.C.; Arús-Pous, J.; Chen, H.; Engkvist, O.; Tyrchan, C.; Bjerrum, E.J. Direct steering of de novo molecular generation with descriptor conditional recurrent neural networks. Nat. Mach. Intell., 2020, 2(5), 254-265.
[http://dx.doi.org/10.1038/s42256-020-0174-5]

Gebauer, N.W.A.; Gastegger, M.; Hessmann, S.S.P.; Müller, K.R.; Schütt, K.T. Inverse design of 3d molecular structures with conditional generative neural networks. Nat. Commun., 2022, 13(1), 973.
[http://dx.doi.org/10.1038/s41467-022-28526-y] [PMID: 35190542]

Galperin, M.Y.; Fernández-Suárez, X.M.; Rigden, D.J. The 24th annual Nucleic Acids Research database issue: a look back and upcoming changes. Nucleic Acids Res., 2017, 45(D1), D1-D11.
[http://dx.doi.org/10.1093/nar/gkw1188] [PMID: 28053160]

Rigden, D.J.; Fernández, X.M. The 2022 Nucleic Acids Research database issue and the online molecular biology database collection. Nucleic Acids Res., 2022, 50(D1), D1-D10.
[http://dx.doi.org/10.1093/nar/gkab1195] [PMID: 34986604]

Deng, J.; Yang, Z.; Ojima, I.; Samaras, D.; Wang, F. Artificial intelligence in drug discovery: Applications and techniques. Brief. Bioinf., 2022, 23(1), bbab430.
[http://dx.doi.org/10.1093/bib/bbab430] [PMID: 34734228]

David, A.; Islam, S.; Tankhilevich, E.; Sternberg, M.J.E. The alphafold database of protein structures: A Biologist’s guide. J. Mol. Biol., 2022, 434(2), 167336.
[http://dx.doi.org/10.1016/j.jmb.2021.167336] [PMID: 34757056]

Aaltonen, L.A.; Abascal, F.; Abeshouse, A.; Aburatani, H.; Adams, D.J.; Agrawal, N.; Ahn, K.S.; Ahn, S-M.; Aikata, H.; Akbani, R.; Akdemir, K.C.; Al-Ahmadie, H.; Al-Sedairy, S.T.; Al-Shahrour, F.; Alawi, M.; Albert, M.; Aldape, K.; Alexandrov, L.B.; Ally, A.; Alsop, K.; Alvarez, E.G.; Amary, F.; Amin, S.B.; Aminou, B.; Ammerpohl, O.; Anderson, M.J.; Ang, Y.; Antonello, D.; Anur, P.; Aparicio, S.; Appelbaum, E.L.; Arai, Y.; Aretz, A.; Arihiro, K.; Ariizumi, S.; Armenia, J.; Arnould, L.; Asa, S.; Assenov, Y.; Atwal, G.; Aukema, S.; Auman, J.T.; Aure, M.R.R.; Awadalla, P.; Aymerich, M.; Bader, G.D.; Baez-Ortega, A.; Bailey, M.H.; Bailey, P.J.; Balasundaram, M.; Balu, S.; Bandopadhayay, P.; Banks, R.E.; Barbi, S.; Barbour, A.P.; Barenboim, J.; Barnholtz-Sloan, J.; Barr, H.; Barrera, E.; Bartlett, J.; Bartolome, J.; Bassi, C.; Bathe, O.F.; Baumhoer, D.; Bavi, P.; Baylin, S.B.; Bazant, W.; Beardsmore, D.; Beck, T.A.; Behjati, S.; Behren, A.; Niu, B.; Bell, C.; Beltran, S.; Benz, C.; Berchuck, A.; Bergmann, A.K.; Bergstrom, E.N.; Berman, B.P.; Berney, D.M.; Bernhart, S.H.; Beroukhim, R.; Berrios, M.; Bersani, S.; Bertl, J.; Betancourt, M.; Bhandari, V.; Bhosle, S.G.; Biankin, A.V.; Bieg, M.; Bigner, D.; Binder, H.; Birney, E.; Birrer, M.; Biswas, N.K.; Bjerkehagen, B.; Bodenheimer, T.; Boice, L.; Bonizzato, G.; De Bono, J.S.; Boot, A.; Bootwalla, M.S.; Borg, A.; Borkhardt, A.; Boroevich, K.A.; Borozan, I.; Borst, C.; Bosenberg, M.; Bosio, M.; Boultwood, J.; Bourque, G.; Boutros, P.C.; Bova, G.S.; Bowen, D.T.; Bowlby, R.; Bowtell, D.D.L.; Boyault, S.; Boyce, R.; Boyd, J.; Brazma, A.; Brennan, P.; Brewer, D.S.; Brinkman, A.B.; Bristow, R.G.; Broaddus, R.R.; Brock, J.E.; Brock, M.; Broeks, A.; Brooks, A.N.; Brooks, D.; Brors, B.; Brunak, S.; Bruxner, T.J.C.; Bruzos, A.L.; Buchanan, A.; Buchhalter, I.; Buchholz, C.; Bullman, S.; Burke, H.; Burkhardt, B.; Burns, K.H.; Busanovich, J.; Bustamante, C.D.; Butler, A.P.; Butte, A.J.; Byrne, N.J.; Børresen-Dale, A-L.; Caesar-Johnson, S.J.; Cafferkey, A.; Cahill, D.; Calabrese, C.; Caldas, C.; Calvo, F.; Camacho, N.; Campbell, P.J.; Campo, E.; Cantù, C.; Cao, S.; Carey, T.E.; Carlevaro-Fita, J.; Carlsen, R.; Cataldo, I.; Cazzola, M.; Cebon, J.; Cerfolio, R.; Chadwick, D.E.; Chakravarty, D.; Chalmers, D.; Chan, C.W.Y.; Chan, K.; Chan-Seng-Yue, M.; Chandan, V.S.; Chang, D.K.; Chanock, S.J.; Chantrill, L.A.; Chateigner, A.; Chatterjee, N.; Chayama, K.; Chen, H-W.; Chen, J.; Chen, K.; Chen, Y.; Chen, Z.; Cherniack, A.D.; Chien, J.; Chiew, Y-E.; Chin, S-F.; Cho, J.; Cho, S.; Choi, J.K.; Choi, W.; Chomienne, C.; Chong, Z.; Choo, S.P.; Chou, A.; Christ, A.N.; Christie, E.L.; Chuah, E.; Cibulskis, C.; Cibulskis, K.; Cingarlini, S.; Clapham, P.; Claviez, A.; Cleary, S.; Cloonan, N.; Cmero, M.; Collins, C.C.; Connor, A.A.; Cooke, S.L.; Cooper, C.S.; Cope, L.; Corbo, V.; Cordes, M.G.; Cordner, S.M.; Cortés-Ciriano, I.; Covington, K.; Cowin, P.A.; Craft, B.; Craft, D.; Creighton, C.J.; Cun, Y.; Curley, E.; Cutcutache, I.; Czajka, K.; Czerniak, B.; Dagg, R.A.; Danilova, L.; Davi, M.V.; Davidson, N.R.; Davies, H.; Davis, I.J.; Davis-Dusenbery, B.N.; Dawson, K.J.; De La Vega, F.M.; De Paoli-Iseppi, R.; Defreitas, T.; Tos, A.P.D.; Delaneau, O.; Demchok, J.A.; Demeulemeester, J.; Demidov, G.M.; Demircioğlu, D.; Dennis, N.M.; Denroche, R.E.; Dentro, S.C.; Desai, N.; Deshpande, V.; Deshwar, A.G.; Desmedt, C.; Deu-Pons, J.; Dhalla, N.; Dhani, N.C.; Dhingra, P.; Dhir, R.; DiBiase, A.; Diamanti, K.; Ding, L.; Ding, S.; Dinh, H.Q.; Dirix, L.; Doddapaneni, H.V.; Donmez, N.; Dow, M.T.; Drapkin, R.; Drechsel, O.; Drews, R.M.; Serge, S.; Dudderidge, T.; Dueso-Barroso, A.; Dunford, A.J.; Dunn, M.; Dursi, L.J.; Duthie, F.R.; Dutton-Regester, K.; Eagles, J.; Easton, D.F.; Edmonds, S.; Edwards, P.A.; Edwards, S.E.; Eeles, R.A.; Ehinger, A.; Eils, J.; Eils, R.; El-Naggar, A.; Eldridge, M.; Ellrott, K.; Erkek, S.; Escaramis, G.; Espiritu, S.M.G.; Estivill, X.; Etemadmoghadam, D.; Eyfjord, J.E.; Faltas, B.M.; Fan, D.; Fan, Y.; Faquin, W.C.; Farcas, C.; Fassan, M.; Fatima, A.; Favero, F.; Fayzullaev, N.; Felau, I.; Fereday, S.; Ferguson, M.L.; Ferretti, V.; Feuerbach, L.; Field, M.A.; Fink, J.L.; Finocchiaro, G.; Fisher, C.; Fittall, M.W.; Fitzgerald, A.; Fitzgerald, R.C.; Flanagan, A.M.; Fleshner, N.E.; Flicek, P.; Foekens, J.A.; Fong, K.M.; Fonseca, N.A.; Foster, C.S.; Fox, N.S.; Fraser, M.; Frazer, S.; Frenkel-Morgenstern, M.; Friedman, W.; Frigola, J.; Fronick, C.C.; Fujimoto, A.; Fujita, M.; Fukayama, M.; Fulton, L.A.; Fulton, R.S.; Furuta, M.; Futreal, P.A.; Füllgrabe, A.; Gabriel, S.B.; Gallinger, S.; Gambacorti-Passerini, C.; Gao, J.; Gao, S.; Garraway, L.; Garred, Ø.; Garrison, E.; Garsed, D.W.; Gehlenborg, N.; Gelpi, J.L.L.; George, J.; Gerhard, D.S.; Gerhauser, C.; Gershenwald, J.E.; Gerstein, M.; Gerstung, M.; Getz, G.; Ghori, M.; Ghossein, R.; Giama, N.H.; Gibbs, R.A.; Gibson, B.; Gill, A.J.; Gill, P.; Giri, D.D.; Glodzik, D.; Gnanapragasam, V.J.; Goebler, M.E.; Goldman, M.J.; Gomez, C.; Gonzalez, S.; Gonzalez-Perez, A.; Gordenin, D.A.; Gossage, J.; Gotoh, K.; Govindan, R.; Grabau, D.; Graham, J.S.; Grant, R.C.; Green, A.R.; Green, E.; Greger, L.; Grehan, N.; Grimaldi, S.; Grimmond, S.M.; Grossman, R.L.; Grundhoff, A.; Gundem, G.; Guo, Q.; Gupta, M.; Gupta, S.; Gut, I.G.; Gut, M.; Göke, J.; Ha, G.; Haake, A.; Haan, D.; Haas, S.; Haase, K.; Haber, J.E.; Habermann, N.; Hach, F.; Haider, S.; Hama, N.; Hamdy, F.C.; Hamilton, A.; Hamilton, M.P.; Han, L.; Hanna, G.B.; Hansmann, M.; Haradhvala, N.J.; Harismendy, O.; Harliwong, I.; Harmanci, A.O.; Harrington, E.; Hasegawa, T.; Haussler, D.; Hawkins, S.; Hayami, S.; Hayashi, S.; Hayes, D.N.; Hayes, S.J.; Hayward, N.K.; Hazell, S.; He, Y.; Heath, A.P.; Heath, S.C.; Hedley, D.; Hegde, A.M.; Heiman, D.I.; Heinold, M.C.; Heins, Z.; Heisler, L.E.; Hellstrom-Lindberg, E.; Helmy, M.; Heo, S.G.; Hepperla, A.J.; Heredia-Genestar, J.M.; Herrmann, C.; Hersey, P.; Hess, J.M.; Hilmarsdottir, H.; Hinton, J.; Hirano, S.; Hiraoka, N.; Hoadley, K.A.; Hobolth, A.; Hodzic, E.; Hoell, J.I.; Hoffmann, S.; Hofmann, O.; Holbrook, A.; Holik, A.Z.; Hollingsworth, M.A.; Holmes, O.; Holt, R.A.; Hong, C.; Hong, E.P.; Hong, J.H.; Hooijer, G.K.; Hornshøj, H.; Hosoda, F.; Hou, Y.; Hovestadt, V.; Howat, W.; Hoyle, A.P.; Hruban, R.H.; Hu, J.; Hu, T.; Hua, X.; Huang, K.; Huang, M.; Huang, M.N.; Huang, V.; Huang, Y.; Huber, W.; Hudson, T.J.; Hummel, M.; Hung, J.A.; Huntsman, D.; Hupp, T.R.; Huse, J.; Huska, M.R.; Hutter, B.; Hutter, C.M.; Hübschmann, D.; Iacobuzio-Donahue, C.A.; Imbusch, C.D.; Imielinski, M.; Imoto, S.; Isaacs, W.B.; Isaev, K.; Ishikawa, S.; Iskar, M.; Islam, S.M.A.; Ittmann, M.; Ivkovic, S.; Izarzugaza, J.M.G.; Jacquemier, J.; Jakrot, V.; Jamieson, N.B.; Jang, G.H.; Jang, S.J.; Jayaseelan, J.C.; Jayasinghe, R.; Jefferys, S.R.; Jegalian, K.; Jennings, J.L.; Jeon, S-H.; Jerman, L.; Ji, Y.; Jiao, W.; Johansson, P.A.; Johns, A.L.; Johns, J.; Johnson, R.; Johnson, T.A.; Jolly, C.; Joly, Y.; Jonasson, J.G.; Jones, C.D.; Jones, D.R.; Jones, D.T.W.; Jones, N.; Jones, S.J.M.; Jonkers, J.; Ju, Y.S.; Juhl, H.; Jung, J.; Juul, M.; Juul, R.I.; Juul, S.; Jäger, N.; Kabbe, R.; Kahles, A.; Kahraman, A.; Kaiser, V.B.; Kakavand, H.; Kalimuthu, S.; von Kalle, C.; Kang, K.J.; Karaszi, K.; Karlan, B.; Karlić, R.; Karsch, D.; Kasaian, K.; Kassahn, K.S.; Katai, H.; Kato, M.; Katoh, H.; Kawakami, Y.; Kay, J.D.; Kazakoff, S.H.; Kazanov, M.D.; Keays, M.; Kebebew, E.; Kefford, R.F.; Kellis, M.; Kench, J.G.; Kennedy, C.J.; Kerssemakers, J.N.A.; Khoo, D.; Khoo, V.; Khuntikeo, N.; Khurana, E.; Kilpinen, H.; Kim, H.K.; Kim, H-L.; Kim, H-Y.; Kim, H.; Kim, J.; Kim, J.; Kim, J.K.; Kim, Y.; King, T.A.; Klapper, W.; Kleinheinz, K.; Klimczak, L.J.; Knappskog, S.; Kneba, M.; Knoppers, B.M.; Koh, Y.; Komorowski, J.; Komura, D.; Komura, M.; Kong, G.; Kool, M.; Korbel, J.O.; Korchina, V.; Korshunov, A.; Koscher, M.; Koster, R.; Kote-Jarai, Z.; Koures, A.; Kovacevic, M.; Kremeyer, B.; Kretzmer, H.; Kreuz, M.; Krishnamurthy, S.; Kube, D.; Kumar, K.; Kumar, P.; Kumar, S.; Kumar, Y.; Kundra, R.; Kübler, K.; Küppers, R.; Lagergren, J.; Lai, P.H.; Laird, P.W.; Lakhani, S.R.; Lalansingh, C.M.; Lalonde, E.; Lamaze, F.C.; Lambert, A.; Lander, E.; Landgraf, P.; Landoni, L.; Langerød, A.; Lanzós, A.; Larsimont, D.; Larsson, E.; Lathrop, M.; Lau, L.M.S.; Lawerenz, C.; Lawlor, R.T.; Lawrence, M.S.; Lazar, A.J.; Lazic, A.M.; Le, X.; Lee, D.; Lee, D.; Lee, E.A.; Lee, H.J.; Lee, J.J-K.; Lee, J-Y.; Lee, J.; Lee, M.T.M.; Lee-Six, H.; Lehmann, K-V.; Lehrach, H.; Lenze, D.; Leonard, C.R.; Leongamornlert, D.A.; Leshchiner, I.; Letourneau, L.; Letunic, I.; Levine, D.A.; Lewis, L.; Ley, T.; Li, C.; Li, C.H.; Li, H.I.; Li, J.; Li, L.; Li, S.; Li, S.; Li, X.; Li, X.; Li, X.; Li, Y.; Liang, H.; Liang, S-B.; Lichter, P.; Lin, P.; Lin, Z.; Linehan, W.M.; Lingjærde, O.C.; Liu, D.; Liu, E.M.; Liu, F-F.F.; Liu, F.; Liu, J.; Liu, X.; Livingstone, J.; Livitz, D.; Livni, N.; Lochovsky, L.; Loeffler, M.; Long, G.V.; Lopez-Guillermo, A.; Lou, S.; Louis, D.N.; Lovat, L.B.; Lu, Y.; Lu, Y-J.; Lu, Y.; Luchini, C.; Lungu, I.; Luo, X.; Luxton, H.J.; Lynch, A.G.; Lype, L.; López, C.; López-Otín, C.; Ma, E.Z.; Ma, Y.; MacGrogan, G.; MacRae, S.; Macintyre, G.; Madsen, T.; Maejima, K.; Mafficini, A.; Maglinte, D.T.; Maitra, A.; Majumder, P.P.; Malcovati, L.; Malikic, S.; Malleo, G.; Mann, G.J.; Mantovani-Löffler, L.; Marchal, K.; Marchegiani, G.; Mardis, E.R.; Margolin, A.A.; Marin, M.G.; Markowetz, F.; Markowski, J.; Marks, J.; Marques-Bonet, T.; Marra, M.A.; Marsden, L.; Martens, J.W.M.; Martin, S.; Martin-Subero, J.I.; Martincorena, I.; Martinez- Fundichely, A.; Maruvka, Y.E.; Mashl, R.J.; Massie, C.E.; Matthew, T.J.; Matthews, L.; Mayer, E.; Mayes, S.; Mayo, M.; Mbabaali, F.; McCune, K.; McDermott, U.; McGillivray, P.D.; McLellan, M.D.; McPherson, J.D.; McPherson, J.R.; McPherson, T.A.; Meier, S.R.; Meng, A.; Meng, S.; Menzies, A.; Merrett, N.D.; Merson, S.; Meyerson, M.; Meyerson, W.; Mieczkowski, P.A.; Mihaiescu, G.L.; Mijalkovic, S.; Mikkelsen, T.; Milella, M.; Mileshkin, L.; Miller, C.A.; Miller, D.K.; Miller, J.K.; Mills, G.B.; Milovanovic, A.; Minner, S.; Miotto, M.; Arnau, G.M.; Mirabello, L.; Mitchell, C.; Mitchell, T.J.; Miyano, S.; Miyoshi, N.; Mizuno, S.; Molnár-Gábor, F.; Moore, M.J.; Moore, R.A.; Morganella, S.; Morris, Q.D.; Morrison, C.; Mose, L.E.; Moser, C.D.; Muiños, F.; Mularoni, L.; Mungall, A.J.; Mungall, K.; Musgrove, E.A.; Mustonen, V.; Mutch, D.; Muyas, F.; Muzny, D.M.; Muñoz, A.; Myers, J.; Myklebost, O.; Möller, P.; Nagae, G.; Nagrial, A.M.; Nahal- Bose, H.K.; Nakagama, H.; Nakagawa, H.; Nakamura, H.; Nakamura, T.; Nakano, K.; Nandi, T.; Nangalia, J.; Nastic, M.; Navarro, A.; Navarro, F.C.P.; Neal, D.E.; Nettekoven, G.; Newell, F.; Newhouse, S.J.; Newton, Y.; Ng, A.W.T.; Ng, A.; Nicholson, J.; Nicol, D.; Nie, Y.; Nielsen, G.P.; Nielsen, M.M.; Nik-Zainal, S.; Noble, M.S.; Nones, K.; Northcott, P.A.; Notta, F.; O’Connor, B.D.; O’Donnell, P.; O’Donovan, M.; O’Meara, S.; O’Neill, B.P.; O’Neill, J.R.; Ocana, D.; Ochoa, A.; Oesper, L.; Ogden, C.; Ohdan, H.; Ohi, K.; Ohno-Machado, L.; Oien, K.A.; Ojesina, A.I.; Ojima, H.; Okusaka, T.; Omberg, L.; Ong, C.K.; Ossowski, S.; Ott, G.; Ouellette, B.F.F.; P’ng, C.; Paczkowska, M.; Paiella, S.; Pairojkul, C.; Pajic, M.; Pan-Hammarström, Q.; Papaemmanuil, E.; Papatheodorou, I.; Paramasivam, N.; Park, J.W.; Park, J-W.; Park, K.; Park, K.; Park, P.J.; Parker, J.S.; Parsons, S.L.; Pass, H.; Pasternack, D.; Pastore, A.; Patch, A-M.; Pauporté, I.; Pea, A.; Pearson, J.V.; Pedamallu, C.S.; Pedersen, J.S.; Pederzoli, P.; Peifer, M.; Pennell, N.A.; Perou, C.M.; Perry, M.D.; Petersen, G.M.; Peto, M.; Petrelli, N.; Petryszak, R.; Pfister, S.M.; Phillips, M.; Pich, O.; Pickett, H.A.; Pihl, T.D.; Pillay, N.; Pinder, S.; Pinese, M.; Pinho, A.V.; Pitkänen, E.; Pivot, X.; Piñeiro-Yáñez, E.; Planko, L.; Plass, C.; Polak, P.; Pons, T.; Popescu, I.; Potapova, O.; Prasad, A.; Preston, S.R.; Prinz, M.; Pritchard, A.L.; Prokopec, S.D.; Provenzano, E.; Puente, X.S.; Puig, S.; Puiggròs, M.; Pulido-Tamayo, S.; Pupo, G.M.; Purdie, C.A.; Quinn, M.C.; Rabionet, R.; Rader, J.S.; Radlwimmer, B.; Radovic, P.; Raeder, B.; Raine, K.M.; Ramakrishna, M.; Ramakrishnan, K.; Ramalingam, S.; Raphael, B.J.; Rathmell, W.K.; Rausch, T.; Reifenberger, G.; Reimand, J.; Reis-Filho, J.; Reuter, V.; Reyes-Salazar, I.; Reyna, M.A.; Reynolds, S.M.; Rheinbay, E.; Riazalhosseini, Y.; Richardson, A.L.; Richter, J.; Ringel, M.; Ringnér, M.; Rino, Y.; Rippe, K.; Roach, J.; Roberts, L.R.; Roberts, N.D.; Roberts, S.A.; Robertson, A.G.; Robertson, A.J.; Rodriguez, J.B.; Rodriguez-Martin, B.; Rodríguez-González, F.G.; Roehrl, M.H.A.; Rohde, M.; Rokutan, H.; Romieu, G.; Rooman, I.; Roques, T.; Rosebrock, D.; Rosenberg, M.; Rosenstiel, P.C.; Rosenwald, A.; Rowe, E.W.; Royo, R.; Rozen, S.G.; Rubanova, Y.; Rubin, M.A.; Rubio-Perez, C.; Rudneva, V.A.; Rusev, B.C.; Ruzzenente, A.; Rätsch, G.; Sabarinathan, R.; Sabelnykova, V.Y.; Sadeghi, S.; Sahinalp, S.C.; Saini, N.; Saito-Adachi, M.; Saksena, G.; Salcedo, A.; Salgado, R.; Salichos, L.; Sallari, R.; Saller, C.; Salvia, R.; Sam, M.; Samra, J.S.; Sanchez-Vega, F.; Sander, C.; Sanders, G.; Sarin, R.; Sarrafi, I.; Sasaki-Oku, A.; Sauer, T.; Sauter, G.; Saw, R.P.M.; Scardoni, M.; Scarlett, C.J.; Scarpa, A.; Scelo, G.; Schadendorf, D.; Schein, J.E.; Schilhabel, M.B.; Schlesner, M.; Schlomm, T.; Schmidt, H.K.; Schramm, S-J.; Schreiber, S.; Schultz, N.; Schumacher, S.E.; Schwarz, R.F.; Scolyer, R.A.; Scott, D.; Scully, R.; Seethala, R.; Segre, A.V.; Selander, I.; Semple, C.A.; Senbabaoglu, Y.; Sengupta, S.; Sereni, E.; Serra, S.; Sgroi, D.C.; Shackleton, M.; Shah, N.C.; Shahabi, S.; Shang, C.A.; Shang, P.; Shapira, O.; Shelton, T.; Shen, C.; Shen, H.; Shepherd, R.; Shi, R.; Shi, Y.; Shiah, Y-J.; Shibata, T.; Shih, J.; Shimizu, E.; Shimizu, K.; Shin, S.J.; Shiraishi, Y.; Shmaya, T.; Shmulevich, I.; Shorser, S.I.; Short, C.; Shrestha, R.; Shringarpure, S.S.; Shriver, C.; Shuai, S.; Sidiropoulos, N.; Siebert, R.; Sieuwerts, A.M.; Sieverling, L.; Signoretti, S.; Sikora, K.O.; Simbolo, M.; Simon, R.; Simons, J.V.; Simpson, J.T.; Simpson, P.T.; Singer, S.; Sinnott-Armstrong, N.; Sipahimalani, P.; Skelly, T.J.; Smid, M.; Smith, J.; Smith-McCune, K.; Socci, N.D.; Sofia, H.J.; Soloway, M.G.; Song, L.; Sood, A.K.; Sothi, S.; Sotiriou, C.; Soulette, C.M.; Span, P.N.; Spellman, P.T.; Sperandio, N.; Spillane, A.J.; Spiro, O.; Spring, J.; Staaf, J.; Stadler, P.F.; Staib, P.; Stark, S.G.; Stebbings, L.; Stefánsson, Ó.A.; Stegle, O.; Stein, L.D.; Stenhouse, A.; Stewart, C.; Stilgenbauer, S.; Stobbe, M.D.; Stratton, M.R.; Stretch, J.R.; Struck, A.J.; Stuart, J.M.; Stunnenberg, H.G.; Su, H.; Su, X.; Sun, R.X.; Sungalee, S.; Susak, H.; Suzuki, A.; Sweep, F.; Szczepanowski, M.; Sültmann, H.; Yugawa, T.; Tam, A.; Tamborero, D.; Tan, B.K.T.; Tan, D.; Tan, P.; Tanaka, H.; Taniguchi, H.; Tanskanen, T.J.; Tarabichi, M.; Tarnuzzer, R.; Tarpey, P.; Taschuk, M.L.; Tatsuno, K.; Tavaré, S.; Taylor, D.F.; Taylor-Weiner, A.; Teague, J.W.; Teh, B.T.; Tembe, V.; Temes, J.; Thai, K.; Thayer, S.P.; Thiessen, N.; Thomas, G.; Thomas, S.; Thompson, A.; Thompson, A.M.; Thompson, J.F.F.; Thompson, R.H.; Thorne, H.; Thorne, L.B.; Thorogood, A.; Tiao, G.; Tijanic, N.; Timms, L.E.; Tirabosco, R.; Tojo, M.; Tommasi, S.; Toon, C.W.; Toprak, U.H.; Torrents, D.; Tortora, G.; Tost, J.; Totoki, Y.; Townend, D.; Traficante, N.; Treilleux, I.; Trotta, J-R.; Trümper, L.H.P.; Tsao, M.; Tsunoda, T.; Tubio, J.M.C.; Tucker, O.; Turkington, R.; Turner, D.J.; Tutt, A.; Ueno, M.; Ueno, N.T.; Umbricht, C.; Umer, H.M.; Underwood, T.J.; Urban, L.; Urushidate, T.; Ushiku, T.; Uusküla-Reimand, L.; Valencia, A.; Van Den Berg, D.J.; Van Laere, S.; Van Loo, P.; Van Meir, E.G.; Van den Eynden, G.G.; Van der Kwast, T.; Vasudev, N.; Vazquez, M.; Vedururu, R.; Veluvolu, U.; Vembu, S.; Verbeke, L.P.C.; Vermeulen, P.; Verrill, C.; Viari, A.; Vicente, D.; Vicentini, C.; VijayRaghavan, K.; Viksna, J.; Vilain, R.E.; Villasante, I.; Vincent-Salomon, A.; Visakorpi, T.; Voet, D.; Vyas, P.; Vázquez-García, I.; Waddell, N.M.; Waddell, N.; Wadelius, C.; Wadi, L.; Wagener, R.; Wala, J.A.; Wang, J.; Wang, J.; Wang, L.; Wang, Q.; Wang, W.; Wang, Y.; Wang, Z.; Waring, P.M.; Warnatz, H-J.; Warrell, J.; Warren, A.Y.; Waszak, S.M.; Wedge, D.C.; Weichenhan, D.; Weinberger, P.; Weinstein, J.N.; Weischenfeldt, J.; Weisenberger, D.J.; Welch, I.; Wendl, M.C.; Werner, J.; Whalley, J.P.; Wheeler, D.A.; Whitaker, H.C.; Wigle, D.; Wilkerson, M.D.; Williams, A.; Wilmott, J.S.; Wilson, G.W.; Wilson, J.M.; Wilson, R.K.; Winterhoff, B.; Wintersinger, J.A.; Wiznerowicz, M.; Wolf, S.; Wong, B.H.; Wong, T.; Wong, W.; Woo, Y.; Wood, S.; Wouters, B.G.; Wright, A.J.; Wright, D.W.; Wright, M.H.; Wu, C-L.; Wu, D-Y.; Wu, G.; Wu, J.; Wu, K.; Wu, Y.; Wu, Z.; Xi, L.; Xia, T.; Xiang, Q.; Xiao, X.; Xing, R.; Xiong, H.; Xu, Q.; Xu, Y.; Xue, H.; Yachida, S.; Yakneen, S.; Yamaguchi, R.; Yamaguchi, T.N.; Yamamoto, M.; Yamamoto, S.; Yamaue, H.; Yang, F.; Yang, H.; Yang, J.Y.; Yang, L.; Yang, L.; Yang, S.; Yang, T-P.; Yang, Y.; Yao, X.; Yaspo, M-L.; Yates, L.; Yau, C.; Ye, C.; Ye, K.; Yellapantula, V.D.; Yoon, C.J.; Yoon, S-S.; Yousif, F.; Yu, J.; Yu, K.; Yu, W.; Yu, Y.; Yuan, K.; Yuan, Y.; Yuen, D.; Yung, C.K.; Zaikova, O.; Zamora, J.; Zapatka, M.; Zenklusen, J.C.; Zenz, T.; Zeps, N.; Zhang, C-Z.; Zhang, F.; Zhang, H.; Zhang, H.; Zhang, H.; Zhang, J.; Zhang, J.; Zhang, J.; Zhang, X.; Zhang, X.; Zhang, Y.; Zhang, Z.; Zhao, Z.; Zheng, L.; Zheng, X.; Zhou, W.; Zhou, Y.; Zhu, B.; Zhu, H.; Zhu, J.; Zhu, S.; Zou, L.; Zou, X.; deFazio, A.; van As, N.; van Deurzen, C.H.M.; van de Vijver, M.J.; van’t Veer, L.; von Mering, C. Pan-cancer analysis of whole genomes. Nature, 2020, 578(7793), 82-93.
[http://dx.doi.org/10.1038/s41586-020-1969-6] [PMID: 32025007]

Finck, A.; Gill, S.I.; June, C.H. Cancer immunotherapy comes of age and looks for maturity. Nat. Commun., 2020, 11(1), 3325.
[http://dx.doi.org/10.1038/s41467-020-17140-5] [PMID: 32620755]

Sparano, J.A.; Gray, R.J.; Makower, D.F.; Pritchard, K.I.; Albain, K.S.; Hayes, D.F.; Geyer, C.E., Jr; Dees, E.C.; Goetz, M.P.; Olson, J.A., Jr; Lively, T.; Badve, S.S.; Saphner, T.J.; Wagner, L.I.; Whelan, T.J.; Ellis, M.J.; Paik, S.; Wood, W.C.; Ravdin, P.M.; Keane, M.M.; Gomez Moreno, H.L.; Reddy, P.S.; Goggins, T.F.; Mayer, I.A.; Brufsky, A.M.; Toppmeyer, D.L.; Kaklamani, V.G.; Berenberg, J.L.; Abrams, J.; Sledge, G.W., Jr Adjuvant chemotherapy guided by a 21-Gene expression assay in breast cancer. N. Engl. J. Med., 2018, 379(2), 111-121.
[http://dx.doi.org/10.1056/NEJMoa1804710] [PMID: 29860917]

Hoadley, K.A.; Yau, C.; Hinoue, T.; Wolf, D.M.; Lazar, A.J.; Drill, E.; Shen, R.; Taylor, A.M.; Cherniack, A.D.; Thorsson, V.; Akbani, R.; Bowlby, R.; Wong, C.K.; Wiznerowicz, M.; Sanchez-Vega, F.; Robertson, A.G.; Schneider, B.G.; Lawrence, M.S.; Noushmehr, H.; Malta, T.M.; Stuart, J.M.; Benz, C.C.; Laird, P.W.; Caesar-Johnson, S.J.; Demchok, J.A.; Felau, I.; Kasapi, M.; Ferguson, M.L.; Hutter, C.M.; Sofia, H.J.; Tarnuzzer, R.; Wang, Z.; Yang, L.; Zenklusen, J.C.; Zhang, J.J.; Chudamani, S.; Liu, J.; Lolla, L.; Naresh, R.; Pihl, T.; Sun, Q.; Wan, Y.; Wu, Y.; Cho, J.; DeFreitas, T.; Frazer, S.; Gehlenborg, N.; Getz, G.; Heiman, D.I.; Kim, J.; Lawrence, M.S.; Lin, P.; Meier, S.; Noble, M.S.; Saksena, G.; Voet, D.; Zhang, H.; Bernard, B.; Chambwe, N.; Dhankani, V.; Knijnenburg, T.; Kramer, R.; Leinonen, K.; Liu, Y.; Miller, M.; Reynolds, S.; Shmulevich, I.; Thorsson, V.; Zhang, W.; Akbani, R.; Broom, B.M.; Hegde, A.M.; Ju, Z.; Kanchi, R.S.; Korkut, A.; Li, J.; Liang, H.; Ling, S.; Liu, W.; Lu, Y.; Mills, G.B.; Ng, K-S.; Rao, A.; Ryan, M.; Wang, J.; Weinstein, J.N.; Zhang, J.; Abeshouse, A.; Armenia, J.; Chakravarty, D.; Chatila, W.K.; de Bruijn, I.; Gao, J.; Gross, B.E.; Heins, Z.J.; Kundra, R.; La, K.; Ladanyi, M.; Luna, A.; Nissan, M.G.; Ochoa, A.; Phillips, S.M.; Reznik, E.; Sanchez-Vega, F.; Sander, C.; Schultz, N.; Sheridan, R.; Sumer, S.O.; Sun, Y.; Taylor, B.S.; Wang, J.; Zhang, H.; Anur, P.; Peto, M.; Spellman, P.; Benz, C.; Stuart, J.M.; Wong, C.K.; Yau, C.; Hayes, D.N.; Parker, J.S.; Wilkerson, M.D.; Ally, A.; Balasundaram, M.; Bowlby, R.; Brooks, D.; Carlsen, R.; Chuah, E.; Dhalla, N.; Holt, R.; Jones, S.J.M.; Kasaian, K.; Lee, D.; Ma, Y.; Marra, M.A.; Mayo, M.; Moore, R.A.; Mungall, A.J.; Mungall, K.; Robertson, A.G.; Sadeghi, S.; Schein, J.E.; Sipahimalani, P.; Tam, A.; Thiessen, N.; Tse, K.; Wong, T.; Berger, A.C.; Beroukhim, R.; Cherniack, A.D.; Cibulskis, C.; Gabriel, S.B.; Gao, G.F.; Ha, G.; Meyerson, M.; Schumacher, S.E.; Shih, J.; Kucherlapati, M.H.; Kucherlapati, R.S.; Baylin, S.; Cope, L.; Danilova, L.; Bootwalla, M.S.; Lai, P.H.; Maglinte, D.T.; Van Den Berg, D.J.; Weisenberger, D.J.; Auman, J.T.; Balu, S.; Bodenheimer, T.; Fan, C.; Hoadley, K.A.; Hoyle, A.P.; Jefferys, S.R.; Jones, C.D.; Meng, S.; Mieczkowski, P.A.; Mose, L.E.; Perou, A.H.; Perou, C.M.; Roach, J.; Shi, Y.; Simons, J.V.; Skelly, T.; Soloway, M.G.; Tan, D.; Veluvolu, U.; Fan, H.; Hinoue, T.; Laird, P.W.; Shen, H.; Zhou, W.; Bellair, M.; Chang, K.; Covington, K.; Creighton, C.J.; Dinh, H.; Doddapaneni, H.V.; Donehower, L.A.; Drummond, J.; Gibbs, R.A.; Glenn, R.; Hale, W.; Han, Y.; Hu, J.; Korchina, V.; Lee, S.; Lewis, L.; Li, W.; Liu, X.; Morgan, M.; Morton, D.; Muzny, D.; Santibanez, J.; Sheth, M.; Shinbrot, E.; Wang, L.; Wang, M.; Wheeler, D.A.; Xi, L.; Zhao, F.; Hess, J.; Appelbaum, E.L.; Bailey, M.; Cordes, M.G.; Ding, L.; Fronick, C.C.; Fulton, L.A.; Fulton, R.S.; Kandoth, C.; Mardis, E.R.; McLellan, M.D.; Miller, C.A.; Schmidt, H.K.; Wilson, R.K.; Crain, D.; Curley, E.; Gardner, J.; Lau, K.; Mallery, D.; Morris, S.; Paulauskis, J.; Penny, R.; Shelton, C.; Shelton, T.; Sherman, M.; Thompson, E.; Yena, P.; Bowen, J.; Gastier-Foster, J.M.; Gerken, M.; Leraas, K.M.; Lichtenberg, T.M.; Ramirez, N.C.; Wise, L.; Zmuda, E.; Corcoran, N.; Costello, T.; Hovens, C.; Carvalho, A.L.; de Carvalho, A.C.; Fregnani, J.H.; Longatto-Filho, A.; Reis, R.M.; Scapulatempo-Neto, C.; Silveira, H.C.S.; Vidal, D.O.; Burnette, A.; Eschbacher, J.; Hermes, B.; Noss, A.; Singh, R.; Anderson, M.L.; Castro, P.D.; Ittmann, M.; Huntsman, D.; Kohl, B.; Le, X.; Thorp, R.; Andry, C.; Duffy, E.R.; Lyadov, V.; Paklina, O.; Setdikova, G.; Shabunin, A.; Tavobilov, M.; McPherson, C.; Warnick, R.; Berkowitz, R.; Cramer, D.; Feltmate, C.; Horowitz, N.; Kibel, A.; Muto, M.; Raut, C.P.; Malykh, A.; Barnholtz-Sloan, J.S.; Barrett, W.; Devine, K.; Fulop, J.; Ostrom, Q.T.; Shimmel, K.; Wolinsky, Y.; Sloan, A.E.; De Rose, A.; Giuliante, F.; Goodman, M.; Karlan, B.Y.; Hagedorn, C.H.; Eckman, J.; Harr, J.; Myers, J.; Tucker, K.; Zach, L.A.; Deyarmin, B.; Hu, H.; Kvecher, L.; Larson, C.; Mural, R.J.; Somiari, S.; Vicha, A.; Zelinka, T.; Bennett, J.; Iacocca, M.; Rabeno, B.; Swanson, P.; Latour, M.; Lacombe, L.; Têtu, B.; Bergeron, A.; McGraw, M.; Staugaitis, S.M.; Chabot, J.; Hibshoosh, H.; Sepulveda, A.; Su, T.; Wang, T.; Potapova, O.; Voronina, O.; Desjardins, L.; Mariani, O.; Roman-Roman, S.; Sastre, X.; Stern, M-H.; Cheng, F.; Signoretti, S.; Berchuck, A.; Bigner, D.; Lipp, E.; Marks, J.; McCall, S.; McLendon, R.; Secord, A.; Sharp, A.; Behera, M.; Brat, D.J.; Chen, A.; Delman, K.; Force, S.; Khuri, F.; Magliocca, K.; Maithel, S.; Olson, J.J.; Owonikoko, T.; Pickens, A.; Ramalingam, S.; Shin, D.M.; Sica, G.; Van Meir, E.G.; Zhang, H.; Eijckenboom, W.; Gillis, A.; Korpershoek, E.; Looijenga, L.; Oosterhuis, W.; Stoop, H.; van Kessel, K.E.; Zwarthoff, E.C.; Calatozzolo, C.; Cuppini, L.; Cuzzubbo, S.; DiMeco, F.; Finocchiaro, G.; Mattei, L.; Perin, A.; Pollo, B.; Chen, C.; Houck, J.; Lohavanichbutr, P.; Hartmann, A.; Stoehr, C.; Stoehr, R.; Taubert, H.; Wach, S.; Wullich, B.; Kycler, W.; Murawa, D.; Wiznerowicz, M.; Chung, K.; Edenfield, W.J.; Martin, J.; Baudin, E.; Bubley, G.; Bueno, R.; De Rienzo, A.; Richards, W.G.; Kalkanis, S.; Mikkelsen, T.; Noushmehr, H.; Scarpace, L.; Girard, N.; Aymerich, M.; Campo, E.; Giné, E.; Guillermo, A.L.; Van Bang, N.; Hanh, P.T.; Phu, B.D.; Tang, Y.; Colman, H.; Evason, K.; Dottino, P.R.; Martignetti, J.A.; Gabra, H.; Juhl, H.; Akeredolu, T.; Stepa, S.; Hoon, D.; Ahn, K.; Kang, K.J.; Beuschlein, F.; Breggia, A.; Birrer, M.; Bell, D.; Borad, M.; Bryce, A.H.; Castle, E.; Chandan, V.; Cheville, J.; Copland, J.A.; Farnell, M.; Flotte, T.; Giama, N.; Ho, T.; Kendrick, M.; Kocher, J-P.; Kopp, K.; Moser, C.; Nagorney, D.; O’Brien, D.; O’Neill, B.P.; Patel, T.; Petersen, G.; Que, F.; Rivera, M.; Roberts, L.; Smallridge, R.; Smyrk, T.; Stanton, M.; Thompson, R.H.; Torbenson, M.; Yang, J.D.; Zhang, L.; Brimo, F.; Ajani, J.A.; Gonzalez, A.M.A.; Behrens, C.; Bondaruk; Broaddus, R.; Czerniak, B.; Esmaeli, B.; Fujimoto, J.; Gershenwald, J.; Guo, C.; Lazar, A.J.; Logothetis, C.; Meric-Bernstam, F.; Moran, C.; Ramondetta, L.; Rice, D.; Sood, A.; Tamboli, P.; Thompson, T.; Troncoso, P.; Tsao, A.; Wistuba, I.; Carter, C.; Haydu, L.; Hersey, P.; Jakrot, V.; Kakavand, H.; Kefford, R.; Lee, K.; Long, G.; Mann, G.; Quinn, M.; Saw, R.; Scolyer, R.; Shannon, K.; Spillane, A.; Stretch, J.; Synott, M.; Thompson, J.; Wilmott, J.; Al-Ahmadie, H.; Chan, T.A.; Ghossein, R.; Gopalan, A.; Levine, D.A.; Reuter, V.; Singer, S.; Singh, B.; Tien, N.V.; Broudy, T.; Mirsaidi, C.; Nair, P.; Drwiega, P.; Miller, J.; Smith, J.; Zaren, H.; Park, J-W.; Hung, N.P.; Kebebew, E.; Linehan, W.M.; Metwalli, A.R.; Pacak, K.; Pinto, P.A.; Schiffman, M.; Schmidt, L.S.; Vocke, C.D.; Wentzensen, N.; Worrell, R.; Yang, H.; Moncrieff, M.; Goparaju, C.; Melamed, J.; Pass, H.; Botnariuc, N.; Caraman, I.; Cernat, M.; Chemencedji, I.; Clipca, A.; Doruc, S.; Gorincioi, G.; Mura, S.; Pirtac, M.; Stancul, I.; Tcaciuc, D.; Albert, M.; Alexopoulou, I.; Arnaout, A.; Bartlett, J.; Engel, J.; Gilbert, S.; Parfitt, J.; Sekhon, H.; Thomas, G.; Rassl, D.M.; Rintoul, R.C.; Bifulco, C.; Tamakawa, R.; Urba, W.; Hayward, N.; Timmers, H.; Antenucci, A.; Facciolo, F.; Grazi, G.; Marino, M.; Merola, R.; de Krijger, R.; Gimenez-Roqueplo, A-P.; Piché, A.; Chevalier, S.; McKercher, G.; Birsoy, K.; Barnett, G.; Brewer, C.; Farver, C.; Naska, T.; Pennell, N.A.; Raymond, D.; Schilero, C.; Smolenski, K.; Williams, F.; Morrison, C.; Borgia, J.A.; Liptay, M.J.; Pool, M.; Seder, C.W.; Junker, K.; Omberg, L.; Dinkin, M.; Manikhas, G.; Alvaro, D.; Bragazzi, M.C.; Cardinale, V.; Carpino, G.; Gaudio, E.; Chesla, D.; Cottingham, S.; Dubina, M.; Moiseenko, F.; Dhanasekaran, R.; Becker, K-F.; Janssen, K-P.; Slotta-Huspenina, J.; Abdel-Rahman, M.H.; Aziz, D.; Bell, S.; Cebulla, C.M.; Davis, A.; Duell, R.; Elder, J.B.; Hilty, J.; Kumar, B.; Lang, J.; Lehman, N.L.; Mandt, R.; Nguyen, P.; Pilarski, R.; Rai, K.; Schoenfield, L.; Senecal, K.; Wakely, P.; Hansen, P.; Lechan, R.; Powers, J.; Tischler, A.; Grizzle, W.E.; Sexton, K.C.; Kastl, A.; Henderson, J.; Porten, S.; Waldmann, J.; Fassnacht, M.; Asa, S.L.; Schadendorf, D.; Couce, M.; Graefen, M.; Huland, H.; Sauter, G.; Schlomm, T.; Simon, R.; Tennstedt, P.; Olabode, O.; Nelson, M.; Bathe, O.; Carroll, P.R.; Chan, J.M.; Disaia, P.; Glenn, P.; Kelley, R.K.; Landen, C.N.; Phillips, J.; Prados, M.; Simko, J.; Smith-McCune, K.; VandenBerg, S.; Roggin, K.; Fehrenbach, A.; Kendler, A.; Sifri, S.; Steele, R.; Jimeno, A.; Carey, F.; Forgie, I.; Mannelli, M.; Carney, M.; Hernandez, B.; Campos, B.; Herold- Mende, C.; Jungk, C.; Unterberg, A.; von Deimling, A.; Bossler, A.; Galbraith, J.; Jacobus, L.; Knudson, M.; Knutson, T.; Ma, D.; Milhem, M.; Sigmund, R.; Godwin, A.K.; Madan, R.; Rosenthal, H.G.; Adebamowo, C.; Adebamowo, S.N.; Boussioutas, A.; Beer, D.; Giordano, T.; Mes- Masson, A-M.; Saad, F.; Bocklage, T.; Landrum, L.; Mannel, R.; Moore, K.; Moxley, K.; Postier, R.; Walker, J.; Zuna, R.; Feldman, M.; Valdivieso, F.; Dhir, R.; Luketich, J.; Pinero, E.M.M.; Quintero-Aguilo, M.; Carlotti, C.G., Jr; Dos Santos, J.S.; Kemp, R.; Sankarankuty, A.; Tirapelli, D.; Catto, J.; Agnew, K.; Swisher, E.; Creaney, J.; Robinson, B.; Shelley, C.S.; Godwin, E.M.; Kendall, S.; Shipman, C.; Bradford, C.; Carey, T.; Haddad, A.; Moyer, J.; Peterson, L.; Prince, M.; Rozek, L.; Wolf, G.; Bowman, R.; Fong, K.M.; Yang, I.; Korst, R.; Rathmell, W.K.; Fantacone-Campbell, J.L.; Hooke, J.A.; Kovatich, A.J.; Shriver, C.D.; DiPersio, J.; Drake, B.; Govindan, R.; Heath, S.; Ley, T.; Van Tine, B.; Westervelt, P.; Rubin, M.A.; Lee, J.I.; Aredes, N.D.; Mariamidze, A. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell, 2018, 173(2), 291-304.e6.
[http://dx.doi.org/10.1016/j.cell.2018.03.022] [PMID: 29625048]

Stanková, K.; Brown, J.S.; Dalton, W.S.; Gatenby, R.A. Optimizing cancer treatment using game theory. JAMA Oncol., 2019, 5(1), 96-103.
[http://dx.doi.org/10.1001/jamaoncol.2018.3395] [PMID: 30098166]

Zehir, A.; Benayed, R.; Shah, R.H.; Syed, A.; Middha, S.; Kim, H.R.; Srinivasan, P.; Gao, J.; Chakravarty, D.; Devlin, S.M.; Hellmann, M.D.; Barron, D.A.; Schram, A.M.; Hameed, M.; Dogan, S.; Ross, D.S.; Hechtman, J.F.; DeLair, D.F.; Yao, J.; Mandelker, D.L.; Cheng, D.T.; Chandramohan, R.; Mohanty, A.S.; Ptashkin, R.N.; Jayakumaran, G.; Prasad, M.; Syed, M.H.; Rema, A.B.; Liu, Z.Y.; Nafa, K.; Borsu, L.; Sadowska, J.; Casanova, J.; Bacares, R.; Kiecka, I.J.; Razumova, A.; Son, J.B.; Stewart, L.; Baldi, T.; Mullaney, K.A.; Al-Ahmadie, H.; Vakiani, E.; Abeshouse, A.A.; Penson, A.V.; Jonsson, P.; Camacho, N.; Chang, M.T.; Won, H.H.; Gross, B.E.; Kundra, R.; Heins, Z.J.; Chen, H.W.; Phillips, S.; Zhang, H.; Wang, J.; Ochoa, A.; Wills, J.; Eubank, M.; Thomas, S.B.; Gardos, S.M.; Reales, D.N.; Galle, J.; Durany, R.; Cambria, R.; Abida, W.; Cercek, A.; Feldman, D.R.; Gounder, M.M.; Hakimi, A.A.; Harding, J.J.; Iyer, G.; Janjigian, Y.Y.; Jordan, E.J.; Kelly, C.M.; Lowery, M.A.; Morris, L.G.T.; Omuro, A.M.; Raj, N.; Razavi, P.; Shoushtari, A.N.; Shukla, N.; Soumerai, T.E.; Varghese, A.M.; Yaeger, R.; Coleman, J.; Bochner, B.; Riely, G.J.; Saltz, L.B.; Scher, H.I.; Sabbatini, P.J.; Robson, M.E.; Klimstra, D.S.; Taylor, B.S.; Baselga, J.; Schultz, N.; Hyman, D.M.; Arcila, M.E.; Solit, D.B.; Ladanyi, M.; Berger, M.F. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat. Med., 2017, 23(6), 703-713.
[http://dx.doi.org/10.1038/nm.4333] [PMID: 28481359]

Cao, Y.; Romero, J.; Aspuru-Guzik, A. Potential of quantum computing for drug discovery. IBM J. Res. Dev., 2018, 62(6), 6:1-6:20.
[http://dx.doi.org/10.1147/JRD.2018.2888987]

Lau, B.; Emani, P.S.; Chapman, J.; Yao, L.; Lam, T.; Merrill, P.; Warrell, J.; Gerstein, M.B.; Lam, H.Y.K. Insights from incorporating quantum computing into drug design workflows. Bioinformatics, 2023, 39(1), btac789.
[http://dx.doi.org/10.1093/bioinformatics/btac789] [PMID: 36477833]