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81. Salomon-Ferrer R, Case DA, Walker RC (2013) An overview of the Amber biomolecular
simulation package. Wiley Interdiscip Rev Comput Mol Sci 3:198–210. https://doi.org/10.
1002/wcms.1121
82. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol
Graph 14(33–8):27–28
83. Wu K, Wei G-W (2018) Quantitative toxicity prediction using topology based multitask deep
neural networks. J Chem Inf Model 58:520–531. https://doi.org/10.1021/acs.jcim.7b00558
84. Huang R, Xia M, Nguyen D-T et al (2016) Tox21Challenge to build predictive models of nuclear
receptor and stress response pathways as mediated by exposure to environmental chemicals
and drugs. Front Environ Sci 3:85. https://doi.org/10.3389/fenvs.2015.00085
85. Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics, 2nd edn. WileyVCH, Weinheim, Germany
86. Damale MG, Harke SN, Kalam Khan FA et al (2014) Recent advances in multidimensional
QSAR (4D-6D): a critical review. Mini Rev Med Chem 14:35–55
87. Verma J, Khedkar VM, Coutinho EC (2010) 3D-QSAR in drug design–a review. Curr Top Med
Chem 10:95–115
88. Hansch C, Maloney PP, Fujita T, Muir RM (1962) Correlation of biological activity of phenoxyacetic acids with hammett substituent constants and partition coefficients. Nature 194:178–180.
https://doi.org/10.1038/194178b0
89. Hong H, Xie Q, Ge W et al (2008) Mold 2 , Molecular descriptors from 2D structures for
chemoinformatics and toxicoinformatics. J Chem Inf Model 48:1337–1344. https://doi.org/10.
1021/ci800038f
90. Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA).
1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967.
https://doi.org/10.1021/ja00226a005
91. Lushington GH, Guo J-X, Wang JL (2007) Whither combine? New opportunities for receptorbased QSAR. Curr Med Chem 14:1863–1877
92. Todeschini R, Gramatica P (1997) The whim theory: new 3d molecular descriptors for QSAR
in environmental modelling. SAR QSAR Environ Res 7:89–115. https://doi.org/10.1080/
10629369708039126
93. Jain AN, Koile K, Chapman D (1994) Compass: predicting biological activities from molecular surface properties. performance comparisons on a steroid benchmark. J Med Chem
37:2316–2327
94. Polanski J, Bak A (2003) Modeling steric and electronic effects in 3D-and 4D-QSAR schemes:
predicting benzoic pK a values and steroid CBG binding affinities. J Chem Inf Comput Sci
43:2081–2092. https://doi.org/10.1021/ci034118l
95. Hopfinger AJ, Wang S, Tokarski JS et al (1997) Construction of 3D-QSAR models using the
4D-QSAR analysis formalism. J Am Chem Soc 119:10509–10524. https://doi.org/10.1021/
JA9718937
96. Vedani A, Dobler M (2002) 5D-QSAR: the key for simulating induced fit? J Med Chem
45:2139–2149. https://doi.org/10.1021/jm011005p
97. Vedani A, Dobler M, Lill MA (2005) Combining protein modeling and 6D-QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor † . J Med Chem
48:3700–3703. https://doi.org/10.1021/jm050185q
98. Da C, Kireev D (2014) Structural protein-ligand interaction fingerprints (SPLIF) for structurebased virtual screening: method and benchmark study. J Chem Inf Model 54:2555–2561. https://
doi.org/10.1021/ci500319f
99. Ash J, Fourches D (2017) Characterizing the chemical space of ERK2 kinase inhibitors using
descriptors computed from molecular dynamics trajectories. J Chem Inf Model 57:1286–1299.
https://doi.org/10.1021/acs.jcim.7b00048
P. Gong et al.
81. Salomon-Ferrer R, Case DA, Walker RC (2013) An overview of the Amber biomolecular
simulation package. Wiley Interdiscip Rev Comput Mol Sci 3:198–210. https://doi.org/10.
1002/wcms.1121
82. Humphrey W, Dalke A, Schulten K (1996) VMD: visual molecular dynamics. J Mol
Graph 14(33–8):27–28
83. Wu K, Wei G-W (2018) Quantitative toxicity prediction using topology based multitask deep
neural networks. J Chem Inf Model 58:520–531. https://doi.org/10.1021/acs.jcim.7b00558
84. Huang R, Xia M, Nguyen D-T et al (2016) Tox21Challenge to build predictive models of nuclear
receptor and stress response pathways as mediated by exposure to environmental chemicals
and drugs. Front Environ Sci 3:85. https://doi.org/10.3389/fenvs.2015.00085
85. Todeschini R, Consonni V (2009) Molecular descriptors for chemoinformatics, 2nd edn. WileyVCH, Weinheim, Germany
86. Damale MG, Harke SN, Kalam Khan FA et al (2014) Recent advances in multidimensional
QSAR (4D-6D): a critical review. Mini Rev Med Chem 14:35–55
87. Verma J, Khedkar VM, Coutinho EC (2010) 3D-QSAR in drug design–a review. Curr Top Med
Chem 10:95–115
88. Hansch C, Maloney PP, Fujita T, Muir RM (1962) Correlation of biological activity of phenoxyacetic acids with hammett substituent constants and partition coefficients. Nature 194:178–180.
https://doi.org/10.1038/194178b0
89. Hong H, Xie Q, Ge W et al (2008) Mold 2 , Molecular descriptors from 2D structures for
chemoinformatics and toxicoinformatics. J Chem Inf Model 48:1337–1344. https://doi.org/10.
1021/ci800038f
90. Cramer RD, Patterson DE, Bunce JD (1988) Comparative molecular field analysis (CoMFA).
1. Effect of shape on binding of steroids to carrier proteins. J Am Chem Soc 110:5959–5967.
https://doi.org/10.1021/ja00226a005
91. Lushington GH, Guo J-X, Wang JL (2007) Whither combine? New opportunities for receptorbased QSAR. Curr Med Chem 14:1863–1877
92. Todeschini R, Gramatica P (1997) The whim theory: new 3d molecular descriptors for QSAR
in environmental modelling. SAR QSAR Environ Res 7:89–115. https://doi.org/10.1080/
10629369708039126
93. Jain AN, Koile K, Chapman D (1994) Compass: predicting biological activities from molecular surface properties. performance comparisons on a steroid benchmark. J Med Chem
37:2316–2327
94. Polanski J, Bak A (2003) Modeling steric and electronic effects in 3D-and 4D-QSAR schemes:
predicting benzoic pK a values and steroid CBG binding affinities. J Chem Inf Comput Sci
43:2081–2092. https://doi.org/10.1021/ci034118l
95. Hopfinger AJ, Wang S, Tokarski JS et al (1997) Construction of 3D-QSAR models using the
4D-QSAR analysis formalism. J Am Chem Soc 119:10509–10524. https://doi.org/10.1021/
JA9718937
96. Vedani A, Dobler M (2002) 5D-QSAR: the key for simulating induced fit? J Med Chem
45:2139–2149. https://doi.org/10.1021/jm011005p
97. Vedani A, Dobler M, Lill MA (2005) Combining protein modeling and 6D-QSAR. Simulating the binding of structurally diverse ligands to the estrogen receptor † . J Med Chem
48:3700–3703. https://doi.org/10.1021/jm050185q
98. Da C, Kireev D (2014) Structural protein-ligand interaction fingerprints (SPLIF) for structurebased virtual screening: method and benchmark study. J Chem Inf Model 54:2555–2561. https://
doi.org/10.1021/ci500319f
99. Ash J, Fourches D (2017) Characterizing the chemical space of ERK2 kinase inhibitors using
descriptors computed from molecular dynamics trajectories. J Chem Inf Model 57:1286–1299.
https://doi.org/10.1021/acs.jcim.7b00048