Index
A
Accessible, 51, 107, 109, 147, 354, 392
Accuracy, 25, 40–42, 45, 46, 69, 111, 121,
122, 124, 126, 127, 132, 152, 161, 216,
217, 226, 260, 261, 263, 268, 269, 291,
320, 328, 371, 388
Activation barriers, 348, 350, 353–356, 358,
359
Activity profile, 8, 59, 279, 281, 286, 293
Adsorption, 24, 30, 341
Adverse drug effect, 289, 290
Adverse outcome, 15, 18, 19, 39, 41, 51,
102–104, 164, 174, 301, 315, 339, 396
Adverse Outcome Pathway (AOP), 18, 37, 38,
42, 44, 45, 51, 52, 103, 107, 108, 169,
174, 301, 302, 308, 317, 390, 391, 396
Agent/individual-based models, 24, 77, 88
Aggregated Computational Toxicology Online
Resource (ACToR), 107, 286
Agonist, 203, 271, 282–285, 322
Algorithms, 3, 5, 6, 26, 27, 51, 59, 84, 109,
122–128, 131, 132, 148, 183, 185, 186,
191, 193, 205, 206, 243, 246, 251, 271,
321, 328, 366, 368, 387, 388, 391, 399
All atom simulations, 185
Allergenic protein database, 242
Ames test, 39, 48, 366, 369, 372, 374
Amino acid sequence, 320
Anatomical therapeutic chemical, 87, 88, 168,
172
Androgen receptor, 107, 109, 203, 270, 282,
283
Animal model, 170, 176, 183
Animal testing, 101, 104, 120, 170, 176, 293,
300, 301
Animal toxicity, 103, 281, 287, 289, 290, 293
Annotation, 82, 84, 85, 147, 148, 152, 166,
168, 169, 247, 260–263, 268, 270–272
Apical endpoints, 18, 20, 50, 51, 299, 301, 302,
304, 305, 308, 310
Applicability domain, 7, 42, 48, 215, 217–220,
222, 223, 229, 371, 375, 395
Application programming interface (API), 385,
388
Aquatic toxicity, 131, 374
Area Under the Curve (AUC), 239
Area Under the Receiver Operating
Characteristic curve (AUC-ROC),
288–290, 292
Artificial intelligence, 1, 4, 20
Aryl radical coupling, 356
Assay, 30, 39, 42, 48, 50, 82, 146–149, 159,
161–172, 174, 270, 272, 279, 281–294,
299, 301, 302, 309, 311
Assay transferability, 6, 159, 164–167,
169–174, 176
Assisted Model Building with Energy
Refinement (AMBER), 25, 185, 186,
188, 190, 191, 198, 203, 322
Austin model, 25
Autoencoder, 130
B
Basic Local Alignment Search Tool (BLAST),
108
Benchmark, 47, 263, 265, 306, 307
Benchmark dose, 303
Big data, 5, 18, 20, 26, 69, 70, 398
Binding affinity, 26, 79, 80, 91, 106, 199, 201,
206, 318, 328, 370
© Springer Nature Switzerland AG 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0
405
A
Accessible, 51, 107, 109, 147, 354, 392
Accuracy, 25, 40–42, 45, 46, 69, 111, 121,
122, 124, 126, 127, 132, 152, 161, 216,
217, 226, 260, 261, 263, 268, 269, 291,
320, 328, 371, 388
Activation barriers, 348, 350, 353–356, 358,
359
Activity profile, 8, 59, 279, 281, 286, 293
Adsorption, 24, 30, 341
Adverse drug effect, 289, 290
Adverse outcome, 15, 18, 19, 39, 41, 51,
102–104, 164, 174, 301, 315, 339, 396
Adverse Outcome Pathway (AOP), 18, 37, 38,
42, 44, 45, 51, 52, 103, 107, 108, 169,
174, 301, 302, 308, 317, 390, 391, 396
Agent/individual-based models, 24, 77, 88
Aggregated Computational Toxicology Online
Resource (ACToR), 107, 286
Agonist, 203, 271, 282–285, 322
Algorithms, 3, 5, 6, 26, 27, 51, 59, 84, 109,
122–128, 131, 132, 148, 183, 185, 186,
191, 193, 205, 206, 243, 246, 251, 271,
321, 328, 366, 368, 387, 388, 391, 399
All atom simulations, 185
Allergenic protein database, 242
Ames test, 39, 48, 366, 369, 372, 374
Amino acid sequence, 320
Anatomical therapeutic chemical, 87, 88, 168,
172
Androgen receptor, 107, 109, 203, 270, 282,
283
Animal model, 170, 176, 183
Animal testing, 101, 104, 120, 170, 176, 293,
300, 301
Animal toxicity, 103, 281, 287, 289, 290, 293
Annotation, 82, 84, 85, 147, 148, 152, 166,
168, 169, 247, 260–263, 268, 270–272
Apical endpoints, 18, 20, 50, 51, 299, 301, 302,
304, 305, 308, 310
Applicability domain, 7, 42, 48, 215, 217–220,
222, 223, 229, 371, 375, 395
Application programming interface (API), 385,
388
Aquatic toxicity, 131, 374
Area Under the Curve (AUC), 239
Area Under the Receiver Operating
Characteristic curve (AUC-ROC),
288–290, 292
Artificial intelligence, 1, 4, 20
Aryl radical coupling, 356
Assay, 30, 39, 42, 48, 50, 82, 146–149, 159,
161–172, 174, 270, 272, 279, 281–294,
299, 301, 302, 309, 311
Assay transferability, 6, 159, 164–167,
169–174, 176
Assisted Model Building with Energy
Refinement (AMBER), 25, 185, 186,
188, 190, 191, 198, 203, 322
Austin model, 25
Autoencoder, 130
B
Basic Local Alignment Search Tool (BLAST),
108
Benchmark, 47, 263, 265, 306, 307
Benchmark dose, 303
Big data, 5, 18, 20, 26, 69, 70, 398
Binding affinity, 26, 79, 80, 91, 106, 199, 201,
206, 318, 328, 370
© Springer Nature Switzerland AG 2019
H. Hong (ed.), Advances in Computational Toxicology, Challenges and Advances
in Computational Chemistry and Physics 30,
https://doi.org/10.1007/978-3-030-16443-0
405