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P. Gong et al.
37. Mcconkey BJ, Sobolev V, Edelman M (2002) The performance of current methods in ligand–protein docking. Curr Sci 83:
38. Maertens A (2014) Green toxicology. Altex 31:243–249. https://doi.org/10.14573/altex.
1406181
39. Adcock SA, McCammon JA (2006) Molecular dynamics: survey of methods for simulating
the activity of proteins. Chem Rev 106:1589–1615. https://doi.org/10.1021/cr040426m
40. Madej T, Lanczycki CJ, Zhang D et al (2014) MMDB and VAST+ : tracking structural similarities between macromolecular complexes. Nucleic Acids Res 42:D297–D303. https://doi.
org/10.1093/nar/gkt1208
41. Berman HM (2008) The protein data bank: a historical perspective. Acta Crystallogr A
64:88–95. https://doi.org/10.1107/S0108767307035623
42. Dutta S, Burkhardt K, Young J et al (2009) Data deposition and annotation at the worldwide
protein data bank. Mol Biotechnol 42:1–13. https://doi.org/10.1007/s12033-008-9127-7
43. McRobb FM, Kufareva I, Abagyan R (2014) In silico identification and pharmacological
evaluation of novel endocrine disrupting chemicals that act via the ligand-binding domain
of the estrogen receptor α. Toxicol Sci 141:188–197. https://doi.org/10.1093/toxsci/kfu114
44. Luo H, Du T, Zhou P et al (2015) Molecular docking to identify associations between drugs
and class I human leukocyte antigens for predicting idiosyncratic drug reactions. Comb Chem
High Throughput Screen 18:296–304
45. Ng HW, Shu M, Luo H et al (2015) Estrogenic activity data extraction and in silico prediction
show the endocrine disruption potential of bisphenol a replacement compounds. Chem Res
Toxicol 28:1784–1795. https://doi.org/10.1021/acs.chemrestox.5b00243
46. Sakkiah S, Kusko R, Pan B et al (2018) Structural changes due to antagonist binding in ligand
binding pocket of androgen receptor elucidated through molecular dynamics simulations. Front
Pharmacol 9:492. https://doi.org/10.3389/fphar.2018.00492
47. Thangapandian S, John S, Sakkiah S, Lee KW (2010) Docking-enabled pharmacophore model
for histone deacetylase 8 inhibitors and its application in anti-cancer drug discovery. J Mol
Graph Model 29:382–395. https://doi.org/10.1016/j.jmgm.2010.07.007
48. Idakwo G, Luttrell J, Chen M et al (2018) A review on machine learning methods for in silico
toxicity prediction. J Environ Sci Heal Part C - Environ Carcinog Ecotoxicol Rev 36:169–191
49. Tang W, Chen J, Wang Z, Xie H, Hong H (2018) Deep learning for predicting toxicity of chemicals: a mini review. J Environ Sci Heal Part C - Environ Carcinog Ecotoxicol Rev 36:252–271
50. Li Y, Idakwo G, Thangapandian S, Chen M, Hong H, Zhang C, Gong P (2018) Target-specific
toxicity knowledgebase (TsTKb): a novel toolkit for in silico predictive toxicology. J Environ
Sci Heal Part C—Environ Carcinog Ecotoxicol Rev 36:219–236
51. Thangapandian S, Idakwo G, Luttrell J, Hong H, Zhang C, Gong P (2019) Quantitative targetspecific toxicity prediction modeling (QTTPM): a proof-of-concept case study on androgen
receptor. Chem Sci (Submitted)
52. Trott O, Olson AJ (2010) AutoDock vina: improving the speed and accuracy of docking with a
new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461.
https://doi.org/10.1002/jcc
53. Roncaglioni A, Toropov AA, Toropova AP, Benfenati E (2013) In silico methods to predict
drug toxicity. Curr Opin Pharmacol 13:802–806. https://doi.org/10.1016/j.coph.2013.06.001
54. Judson RS, Martin MT, Egeghy P et al (2012) Aggregating data for computational toxicology applications: the U.S. environmental protection agency (EPA) aggregated computational
toxicology resource (ACToR) System. Int J Mol Sci 13:1805–1831. https://doi.org/10.3390/
ijms13021805
55. Williams AJ, Grulke CM, Edwards J et al (2017) The comptox chemistry dashboard: a community data resource for environmental chemistry. J Cheminform 9:61. https://doi.org/10.1186/
s13321-017-0247-6
56. Brown N, Cambruzzi J, Cox PJ et al (2018) Big data in drug discovery. Prog Med Chem
57:277–356. https://doi.org/10.1016/bs.pmch.2017.12.003
57. Gaulton A, Hersey A, Nowotka M et al (2017) The ChEMBL database in 2017. Nucleic Acids
Res 45:D945–D954. https://doi.org/10.1093/nar/gkw1074
P. Gong et al.
37. Mcconkey BJ, Sobolev V, Edelman M (2002) The performance of current methods in ligand–protein docking. Curr Sci 83:
38. Maertens A (2014) Green toxicology. Altex 31:243–249. https://doi.org/10.14573/altex.
1406181
39. Adcock SA, McCammon JA (2006) Molecular dynamics: survey of methods for simulating
the activity of proteins. Chem Rev 106:1589–1615. https://doi.org/10.1021/cr040426m
40. Madej T, Lanczycki CJ, Zhang D et al (2014) MMDB and VAST+ : tracking structural similarities between macromolecular complexes. Nucleic Acids Res 42:D297–D303. https://doi.
org/10.1093/nar/gkt1208
41. Berman HM (2008) The protein data bank: a historical perspective. Acta Crystallogr A
64:88–95. https://doi.org/10.1107/S0108767307035623
42. Dutta S, Burkhardt K, Young J et al (2009) Data deposition and annotation at the worldwide
protein data bank. Mol Biotechnol 42:1–13. https://doi.org/10.1007/s12033-008-9127-7
43. McRobb FM, Kufareva I, Abagyan R (2014) In silico identification and pharmacological
evaluation of novel endocrine disrupting chemicals that act via the ligand-binding domain
of the estrogen receptor α. Toxicol Sci 141:188–197. https://doi.org/10.1093/toxsci/kfu114
44. Luo H, Du T, Zhou P et al (2015) Molecular docking to identify associations between drugs
and class I human leukocyte antigens for predicting idiosyncratic drug reactions. Comb Chem
High Throughput Screen 18:296–304
45. Ng HW, Shu M, Luo H et al (2015) Estrogenic activity data extraction and in silico prediction
show the endocrine disruption potential of bisphenol a replacement compounds. Chem Res
Toxicol 28:1784–1795. https://doi.org/10.1021/acs.chemrestox.5b00243
46. Sakkiah S, Kusko R, Pan B et al (2018) Structural changes due to antagonist binding in ligand
binding pocket of androgen receptor elucidated through molecular dynamics simulations. Front
Pharmacol 9:492. https://doi.org/10.3389/fphar.2018.00492
47. Thangapandian S, John S, Sakkiah S, Lee KW (2010) Docking-enabled pharmacophore model
for histone deacetylase 8 inhibitors and its application in anti-cancer drug discovery. J Mol
Graph Model 29:382–395. https://doi.org/10.1016/j.jmgm.2010.07.007
48. Idakwo G, Luttrell J, Chen M et al (2018) A review on machine learning methods for in silico
toxicity prediction. J Environ Sci Heal Part C - Environ Carcinog Ecotoxicol Rev 36:169–191
49. Tang W, Chen J, Wang Z, Xie H, Hong H (2018) Deep learning for predicting toxicity of chemicals: a mini review. J Environ Sci Heal Part C - Environ Carcinog Ecotoxicol Rev 36:252–271
50. Li Y, Idakwo G, Thangapandian S, Chen M, Hong H, Zhang C, Gong P (2018) Target-specific
toxicity knowledgebase (TsTKb): a novel toolkit for in silico predictive toxicology. J Environ
Sci Heal Part C—Environ Carcinog Ecotoxicol Rev 36:219–236
51. Thangapandian S, Idakwo G, Luttrell J, Hong H, Zhang C, Gong P (2019) Quantitative targetspecific toxicity prediction modeling (QTTPM): a proof-of-concept case study on androgen
receptor. Chem Sci (Submitted)
52. Trott O, Olson AJ (2010) AutoDock vina: improving the speed and accuracy of docking with a
new scoring function, efficient optimization, and multithreading. J Comput Chem 31:455–461.
https://doi.org/10.1002/jcc
53. Roncaglioni A, Toropov AA, Toropova AP, Benfenati E (2013) In silico methods to predict
drug toxicity. Curr Opin Pharmacol 13:802–806. https://doi.org/10.1016/j.coph.2013.06.001
54. Judson RS, Martin MT, Egeghy P et al (2012) Aggregating data for computational toxicology applications: the U.S. environmental protection agency (EPA) aggregated computational
toxicology resource (ACToR) System. Int J Mol Sci 13:1805–1831. https://doi.org/10.3390/
ijms13021805
55. Williams AJ, Grulke CM, Edwards J et al (2017) The comptox chemistry dashboard: a community data resource for environmental chemistry. J Cheminform 9:61. https://doi.org/10.1186/
s13321-017-0247-6
56. Brown N, Cambruzzi J, Cox PJ et al (2018) Big data in drug discovery. Prog Med Chem
57:277–356. https://doi.org/10.1016/bs.pmch.2017.12.003
57. Gaulton A, Hersey A, Nowotka M et al (2017) The ChEMBL database in 2017. Nucleic Acids
Res 45:D945–D954. https://doi.org/10.1093/nar/gkw1074