Abstract
With recent trends toward the use of big data for drug discovery-related databases, increased computing scale and speed, and sophisticated computation theory for molecular design, in silico drug discovery is becoming an increasingly practical technique. Prior to 2010, its contribution was limited to the early-stage drug discovery of feasible targets; however, applications of recent new technologies to more difficult and complicated targets have led to the effective drug design in the late stage of the drug discovery process. Herein, after reviewing recent in silico drug discovery that utilizes information science (informatics, including artificial intelligence (AI)) and computational science (biomolecular simulations based on molecular dynamics and quantum mechanics), we introduce actual cases of in silico drug discovery. Finally, we discuss efforts in applying AI to drug discovery, which has become practical recently, and consider potential future developments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shaw DE, Deneroff MM, Dror RO, Kuskin JS, Larson RH, Salmon JK, Young C, Batson B, Bowers KJ, Chao JC, Eastwood MP, Gagliardo J, Grossman JP, Ho CR, Ierardi DJ, Kolossváry I, Klepeis JL, Layman T, McLeavey C, Moraes MA, Mueller R, Priest EC, Shan Y, Spengler J, Theobald M, Towles B, Wang SC (2008) A special-purpose machine for molecular dynamics simulation. Commun ACM 51(7):91–97
Ohmura I, Morimoto G, Ohno Y, Hasegawa A, Taiji M (2014) MDGRAPE-4: a special-purpose computer system for molecular dynamics simulations. Phil Trans R Soc A 372:20130387
Tanrikulu Y, Krüger B, Proschak E (2013) The holistic integration of virtual screening in drug discovery. Drug Discov Today 18:358–364
Sato T, Yuki H, Takaya D, Sasaki S, Tanaka A, Honma T (2012) Application of support vector machine to three-dimensional shape-based virtual screening using comprehensive three-dimensional molecular shape overlay with known inhibitors. J Chem Inf Model 52(4):1015–1026
Sato T, Honma T, Yokoyama S (2010) Combining machine learning and pharmacophore-based interaction fingerprint for in silico screening. J Chem Inf Model 50(1):170–185
Sato T, Watanabe H, Tsuganezawa K, Yuki H, Mikuni J, Yoshikawa S, Kukimoto-Niino M, Fujimoto T, Terazawa Y, Wakiyama M, Kojima H, Okabe T, Nagano T, Shirouzu M, Yokoyama S, Tanaka A, Honma T (2012) Identification of novel drug-resistant EGFR mutant inhibitors by in silico screening using comprehensive assessments of protein structures. Bioorg Med Chem 20(12):3756–3767
Watanabe C, Watanabe H, Fukuzawa K, Parker LJ, Okiyama Y, Yuki H, Yokoyama S, Nakano H, Tanaka S, Honma T (2017) Theoretical analysis on activity cliff among benzofuranone class Pim1 inhibitors using the fragment molecular orbital with molecular mechanics Poisson-Boltzmann surface area (FMO+MM-PBSA) method. J Chem Inf Model 57(12):2996–3010
Okada-Iwabu M, Yamauchi T, Iwabu M, Honma T, Hamagami KI, Matsuda K, Yamaguchi M, Tanabe H, Kimura-Someya T, Shirouzu M, Ogata H, Tokuyama K, Ueki K, Nagano T, Tanaka A, Yokoyama S, Kadowaki T (2013) A small-molecule AdipoR agonist for type 2 diabetes and short life in obesity. Nature 503:493–499
Nakano H, Saito N, Parker L, Tada Y, Abe M, Tsuganezawa K, Yokoyama S, Tanaka A, Kojima H, Okabe T, Nagano T (2012) Rational evolution of a novel type of potent and selective proviral integration site in Moloney murine leukemia virus kinase 1 (PIM1) inhibitor from a screening-hit compound. J Med Chem 55(11):5151
Saito Y, Yuki H, Kuratani M, Hashizume Y, Takagi S, Honma T, Tanaka A, Shirouzu M, Mikuni J, Handa N, Ogahara I, Sone A, Najima Y, Tomabechi Y, Wakiyama M, Uchida N, Tomizawa-Murasawa M, Kaneko A, Tanaka S, Suzuki N, Kajita H, Aoki Y, Ohara O, Shultz LD, Fukami T, Goto T, Taniguchi S, Yokoyama S, Ishikawa F (2013) A pyrrolo-pyrimidine derivative targets human primary AML stem cells in vivo. Sci Trans Med 5:181ra52
Yuki H, Kikuzato K, Koda Y, Mikuni J, Tomabechi Y, Kukimoto-Niino M, Tanaka A, Shirai F, Shirouzu M, Koyama H, Honma T (2017) Activity cliff for 7-substituted pyrrolo-pyrimidine inhibitors of HCK explained in terms of predicted basicity of the amine nitrogen. Bioorg Med Chem 25(16):4259–4264
Inaoka DK, Iida M, Tabuchi T, Honma T, Lee N, Hashimoto S, Matsuoka S, Kuranaga T, Sato K, Shiba T, Sakamoto K, Balogun EO, Suzuki S, Nara T, Rocha JR, Montanari CA, Tanaka A, Inoue M, Kita K (2016) The open form inducer approach for structure-based drug design. PLoS ONE 11(11):e0167078
Inaoka DK, Iida M, Hashimoto S, Tabuchi T, Kuranaga T, Balogun EO, Honma T, Tanaka A, Harada S, Nara T, Kita K, Inoue M (2017) Design and synthesis of potent substrate-based inhibitors of the Trypanosoma cruzi dihydroorotate dehydrogenase. Bioorg Med Chem 25(4):1465–1470
Nishikimi A, Uruno T, Duan X, Cao Q, Okamura Y, Saitoh T, Saito N, Sakaoka S, Du Y, Suenaga A, Kukimoto-Niino M, Miyano K, Gotoh K, Okabe T, Sanematsu F, Tanaka Y, Sumimoto H, Honma T, Yokoyama S, Nagano T, Kohda D, Kanai M, Fukui Y (2012) Blockade of inflammatory responses by a small-molecule inhibitor of the Rac activator DOCK2. Chem Biol 19(4):488–497
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Honma, T. (2021). Recent Advances of In Silico Drug Discovery: Integrated Systems of Informatics and Simulation. In: Mochizuki, Y., Tanaka, S., Fukuzawa, K. (eds) Recent Advances of the Fragment Molecular Orbital Method. Springer, Singapore. https://doi.org/10.1007/978-981-15-9235-5_14
Download citation
DOI: https://doi.org/10.1007/978-981-15-9235-5_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-9234-8
Online ISBN: 978-981-15-9235-5
eBook Packages: Chemistry and Materials ScienceChemistry and Material Science (R0)