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50. He X, Zhu T, Wang X, Liu J, Zhang JZ (2014) Fragment quantum mechanical calculation of
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51. Murugan NA, Nordberg A, Ågren H (2018) Different positron emission tomography tau
tracers bind to multiple binding sites on the tau fibril: insight from computational modeling.
ACS Chem Neurosci 9 (7):1757–1767
52. Yu YB, Privalov PL, Hodges RS (2001) Contribution of translational and rotational motions
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53. von Lilienfeld OA (2018) Quantum machine learning in chemical compound space. Angew
Chem Int Ed 57(16):4164–4169
54. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T (2018) The rise of deep learning in
drug discovery. Drug Disco Today 23(6):1241–1250
55. Townshend RJ, Bedi R, Dror RO (2018) Generalizable protein interface prediction with
end-to-end learning. arXiv preprint arXiv:1807.01297
56. Segler MH, Preuss M, Waller MP (2018) Planning chemical syntheses with deep neural
networks and symbolic AI. Nature 555(7698):604
57. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A (2018) Machine learning for
molecular and materials science. Nature 559(7715):547
58. Smith JS, Isayev O, Roitberg AE (2017) ANI-1: an extensible neural network potential with
DFT accuracy at force field computational cost. Chem Sci 8(4):3192–3203
59. Chattopadhyay A, Zheng M, Waller MP, Priyakumar UD (2018) A probabilistic framework
for constructing temporal relations in replica exchange molecular trajectories. J Chem Theory
Comput 14(7):3365–3380
60. Klepsch F, Vasanthanathan P, Ecker GF (2014) Ligand and structure-based classification
models for Prediction of P-glycoprotein inhibitors. J Chem Inf Model 54:218–229
61. Poongavanam V, Kongsted J (2013) Virtual screening models for prediction of HIV-1 RT
associated RNase H inhibition. PLoS ONE 16(8):e73478. https://doi.org/10.1371/journal.
pone.0073478
62. Vasanthanathan P, Lastdrager J, Oostenbrink C, Commandeur JNM, Vermeulen NPE,
Jørgensen FS, Olsen Lars (2011) Identification of CYP1A2 ligands by structure-based and
ligand-based virtual screening. Med Chem Comm 2:853–859
63. Lavecchia A (2015) Machine-learning approaches in drug discovery: methods and
applications. Drug Disco Today 20(3):318–331
64. Colwell LJ (2018) Statistical and machine learning approaches to predicting protein-ligand
interactions. Curr Opin Struct Biol 49:123–128
65. Wójcikowski M, Ballester PJ, Siedlecki P (2017) Performance of machine-learning scoring
functions in structure-based virtual screening. Sci Rep 7:46710
66. Kinnings SL, Liu N, Tonge PJ, Jackson RM, Xie L, Bourne PE (2011) A machine
learning-based method to improve docking scoring functions and its application to drug
repurposing. J Chem Inf Model 51(2):408–419
67. Ragoza M, Hochuli J, Idrobo E, Sunseri J, Koes DR (2017) Protein-ligand scoring with
convolutional neural networks. J Chem Inf Model 57(4):942–957
68. Popova M, Isayev O, Tropsha A (2018) Deep reinforcement learning for de novo drug design.
Sci Adv 4(7):eaap7885
69. Lo YC, Rensi SE, Torng W, Altman RB (2018) Machine learning in chemoinformatics and
drug discovery. Drug Disco Today 23(8):1538–1546.M
246
N. A. Murugan et al.
Esposito F, Tramontano E, Kongsted J (2018) Structure-guided approach identifies a novel
class of HIV-1 ribonuclease H inhibitors: binding mode insights through magnesium
complexation and site-directed mutagenesis studies. Med Chem Comm 9:562–575
49. Zhang Lei, Li Wei, Fang Tao, Li Shuhua (2017) accurate relative energies and binding
energies of large ice-liquid water clusters and periodic structures. J Phys Chem 121(20):4030–
4038
50. He X, Zhu T, Wang X, Liu J, Zhang JZ (2014) Fragment quantum mechanical calculation of
proteins and its applications. Acc Chem Res 47(9):2748–2757
51. Murugan NA, Nordberg A, Ågren H (2018) Different positron emission tomography tau
tracers bind to multiple binding sites on the tau fibril: insight from computational modeling.
ACS Chem Neurosci 9 (7):1757–1767
52. Yu YB, Privalov PL, Hodges RS (2001) Contribution of translational and rotational motions
to molecular association in aqueous solution. Biophys J 81(3):1632–1642
53. von Lilienfeld OA (2018) Quantum machine learning in chemical compound space. Angew
Chem Int Ed 57(16):4164–4169
54. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T (2018) The rise of deep learning in
drug discovery. Drug Disco Today 23(6):1241–1250
55. Townshend RJ, Bedi R, Dror RO (2018) Generalizable protein interface prediction with
end-to-end learning. arXiv preprint arXiv:1807.01297
56. Segler MH, Preuss M, Waller MP (2018) Planning chemical syntheses with deep neural
networks and symbolic AI. Nature 555(7698):604
57. Butler KT, Davies DW, Cartwright H, Isayev O, Walsh A (2018) Machine learning for
molecular and materials science. Nature 559(7715):547
58. Smith JS, Isayev O, Roitberg AE (2017) ANI-1: an extensible neural network potential with
DFT accuracy at force field computational cost. Chem Sci 8(4):3192–3203
59. Chattopadhyay A, Zheng M, Waller MP, Priyakumar UD (2018) A probabilistic framework
for constructing temporal relations in replica exchange molecular trajectories. J Chem Theory
Comput 14(7):3365–3380
60. Klepsch F, Vasanthanathan P, Ecker GF (2014) Ligand and structure-based classification
models for Prediction of P-glycoprotein inhibitors. J Chem Inf Model 54:218–229
61. Poongavanam V, Kongsted J (2013) Virtual screening models for prediction of HIV-1 RT
associated RNase H inhibition. PLoS ONE 16(8):e73478. https://doi.org/10.1371/journal.
pone.0073478
62. Vasanthanathan P, Lastdrager J, Oostenbrink C, Commandeur JNM, Vermeulen NPE,
Jørgensen FS, Olsen Lars (2011) Identification of CYP1A2 ligands by structure-based and
ligand-based virtual screening. Med Chem Comm 2:853–859
63. Lavecchia A (2015) Machine-learning approaches in drug discovery: methods and
applications. Drug Disco Today 20(3):318–331
64. Colwell LJ (2018) Statistical and machine learning approaches to predicting protein-ligand
interactions. Curr Opin Struct Biol 49:123–128
65. Wójcikowski M, Ballester PJ, Siedlecki P (2017) Performance of machine-learning scoring
functions in structure-based virtual screening. Sci Rep 7:46710
66. Kinnings SL, Liu N, Tonge PJ, Jackson RM, Xie L, Bourne PE (2011) A machine
learning-based method to improve docking scoring functions and its application to drug
repurposing. J Chem Inf Model 51(2):408–419
67. Ragoza M, Hochuli J, Idrobo E, Sunseri J, Koes DR (2017) Protein-ligand scoring with
convolutional neural networks. J Chem Inf Model 57(4):942–957
68. Popova M, Isayev O, Tropsha A (2018) Deep reinforcement learning for de novo drug design.
Sci Adv 4(7):eaap7885
69. Lo YC, Rensi SE, Torng W, Altman RB (2018) Machine learning in chemoinformatics and
drug discovery. Drug Disco Today 23(8):1538–1546.M
246
N. A. Murugan et al.