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Editorial

Is high performance computing a requirement for novel drug discovery and how will this impact academic efforts?

ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon show all
Pages 981-985 | Received 16 Oct 2019, Accepted 17 Apr 2020, Published online: 29 Apr 2020

References

  • Brogi S. Computational approaches for drug discovery. Molecules. 2019;24(17):3061.
  • Schneider G, Clark DE. Automated De Novo drug design: are we nearly there yet? Angew. Chem Int Ed. 2019;58(32):10792–10803.
  • Lyu J, Wang S, Balius TE, et al., Ultra-large library docking for discovering new chemotypes. Nature. 566(7743): 224–229. 2019.
  • Samdani A, Vetrivel U. POAP: A GNU parallel based multithreaded pipeline of open babel and AutoDock suite for boosted high throughput virtual screening. Comput Biol Chem. 2018;74:39–48.
  • Feinstein WP, Brylinski M. Accelerated structural bioinformatics for drug discovery. Boston: Morgan Kaufmann; 2015. DOI:10.1016/B978-0-12-803819-2.00012-4.
  • Imbernón B, Cecilia JM, Pérez-Sánchez H, et al., METADOCK: A parallel metaheuristic schema for virtual screening methods. Int J High Perform Comput Appl. 32(6): 789–803. 2017.
  • Yuan S, Chan JF, den-Haan H, et al. Structure-based discovery of clinically approved drugs as Zika virus NS2B-NS3 protease inhibitors that potently inhibit Zika virus infection in vitro and in vivo. Antiviral Res. 2017;145:33–43.
  • Liu T, Lu D, Zhang H, et al. Applying high-performance computing in drug discovery and molecular simulation. Natl. 2016;3(1):49–63.
  • Dantas RF, Evangelista TCS, Neves BJ, et al., Dealing with frequent hitters in drug discovery: a multidisciplinary view on the issue of filtering compounds on biological screenings. Expert Opin Drug Discov. 14(12): 1269–1282. 2019.
  • Korb O, Finn PW, Jones G. The cloud and other new computational methods to improve molecular modelling. Expert Opin Drug Discov. 2014;9(10):1121–1131.
  • Banegas-Luna AJ, Imbernón B, Llanes AC, et al. Advances in distributed computing with modern drug discovery. Expert Opin Drug Discov. 2019;14(1):9–22.
  • Banegas-Luna AJ, Cerón-Carrasco JP, Puertas-Martín S, et al. BRUSELAS: HPC generic and customizable software architecture for 3D ligand-based virtual screening of large molecular databases. J Chem Inf Model. 2019;59(6):2805–2817.
  • Panjkovich A, Daura X. PARS: a web server for the prediction of protein allosteric and regulatory sites. Bioinformatics. 2014;30(9):1314–1315.
  • Shoichet BK. Virtual screening of chemical libraries. Nature. 2004;432(7019):862–865.
  • Pereira JC, Caffarena ER, Dos Santos ER. Boosting docking-based virtual screening with deep learning. J Chem Inf Model. 2016;56(12):2495–2506.
  • Wójcikowski M, Ballester PJ, Siedlecki P. Performance of machine-learning scoring functions in structure-based virtual screening. Sci Rep. 2017;7(1):46710.
  • Cang Z, Mu L, Wei G-W. Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening. PLoS Comput Biol. 2018;14(1):e1005929.
  • Ragoza M, Hochuli J, Idrobo E, et al. Protein–ligand scoring with convolutional neural networks. J Chem Inf Model. 2017;57(4):942–957.
  • Lima AN, Philot EA, Trossini GHG, et al. Use of machine learning approaches for novel drug discovery. Expert Opin Drug Discov. 2016;11(3):225–239.
  • Lo Y-C, Rensi SE, Torng W, et al. Machine learning in chemoinformatics and drug discovery. Drug Discov Today. 2018;23(8):1538–1546.
  • Arús-Pous J, Blaschke T, Ulander S, et al. Exploring the GDB-13 chemical space using deep generative models. J Cheminform. 2019;11(1):20.
  • Putin E, Asadulaev A, Ivanenkov Y, et al. Reinforced adversarial neural computer for de novo molecular design. J Chem Inf Model. 2018;58(6):1194–1204.
  • Carpenter KA, Cohen DS, Jarrell JT, et al. Deep learning and virtual drug screening. Future Med Chem. 2018;10(21):2557–2567.
  • Lavecchia A. Machine-learning approaches in drug discovery: methods and applications. Drug Discov Today. 2015;20(3):318–331.
  • Schmidhuber J. Deep learning in neural networks: an overview. Neural Netw. 2015;61:85–117.
  • IBM PowerAI. Deep learning unleashed on IBM power systems servers. IBM RedBooks. Mar 2018. [online]. [cited 2020 Feb 17]. Available from: http://www.redbooks.ibm.com/abstracts/sg248409.html?Open
  • Back R, Sere K Superposition refinement of parallel algorithms. In: Parker KR and Rose GA, editors. Formal description techniques, IV. North-Holland Publishing Company; 1992. p. 475–493.
  • The mathematics of quantum-enabled applications on the D-wave quantum computer. Not Am Math Soc. 2019;66(6):1.
  • Perez-Sanchez H, Wenzel W. Optimization methods for virtual screening on novel computational architectures. Curr Comput Aided-Drug Des. 2011;7(1):44–52.

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