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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5387_Библиотеки_им_академика_М_И_Перельмана.pdf
Index
Note: Bold page numbers refer to tables and italic page numbers refer to gures.
AbBERT 92, 10 0, 102 –103
Ab‑CoV database 168
AbLang 10 0, 101, 123, 124, 183
ABlooper 69, 85, 86
ABodyBuilder2 69, 86
AC‑SINS see afnity‑capture
self‑interactionnano‑spectroscopy
(AC‑SINS)
adalimumab 323–34
adenoassociated viruses (AAVs) 319
adoptive cell therapy (ACT) 316
afnity maturation 49, 102, 130, 133–135, 142, 184,
229, 257, 262
afnity‑capture self‑interactionnano‑spectroscopy
(AC‑SINS) 229–231, 234, 234
AgAbDb 167, 168
agent‑based models (ABMs) 287, 291
aggregation, prediction of 93–95
Aggrescan 3D 94
Agilent 2100 Bioanalyser 24
AI see articial intelligence (AI)
AIML see articial intelligence and machine
learning (AIML)
ALCOA (Attributable, Legible, Contemporaneous,
Original, and Accurate) 18
all‑atom simulations 208–210, 214, 216, 219–221
aggregation‑prone antibody therapeutics
220–221
binding mechanism and oligomeric
preference217, 218, 219
energetic interactions 219–220
coarse‑grain simulations 208–210
AlphaFold 28, 44, 49, 206, 346
AlphaFold2 (AF2) 49, 51, 69, 85, 130, 145, 183,
184, 271, 272, 345
Amazon Web Services (AWS) 34
Amyloid Light Chain Database (AL‑Base) 167, 168
AntiBERTa 88, 100, 101–102, 183
AntiBERTy 69, 100, 102
antibody 77, 116
based biotherapeutics 3, 77, 78
design
biophysical parameters 180 –182
de novo 270–272, 271
machine learning 117
discovery
antigen generation strategies 45–46
computational methods 46–50
computational protein structure prediction
46–50, 49
in vitro strategies 41
in vivo strategies 41
machine learning in 84–96
next‑generation sequencing for 60–71
target proteins for 41, 41–45, 43
diversity 99
function
machine learning 136, 137–138, 139 –141
structure‑based simulations 130, 131–132,
133–135
generation
B‑cell repertoires 119–121, 121
deep generative models 122–124, 123
large language models 122
sequences 119
representations 97–98
sequence alignment 98
antibody‑antigen complex 119, 130, 133, 141, 142
structure prediction, docking tools 168–169
antibody–antigen interaction 133–134
computational approaches 169, 170, 171
mutational scanning 182
antibody‑binding prediction, by deep learning
docking 89
epitope prediction 88–89
paratope prediction 88
from sequence89–90
simulated Ab–Ag binding data 90
antibody‑drug conjugates (ADCs) 8, 51, 53, 274,
314–316
antibody drug discovery, computational workow 117
antibody formats 5, 142–144
antibody generation, by language models
AbBERT 10 0, 102 –103
AbLang 10 0, 101
AntiBERTa 100, 101–102
Anti BERTy 100, 102
BioPhi 100, 103
Immune2vec 100, 100
ProtBERT 100, 101
ProtVec 99, 10 0
transformer architectures 101
361
362 Index
antibody structure‑function 164
biophysical parameters 180 –182
computational methods 168–169, 170, 171
in silico prediction 171, 172–176, 177–180
language models 182–184, 183
molecular dynamics simulations 184
mutational scanning 182
resources and databases 165, 166 –167, 168
anti‑drug antibodies (ADAs) 91, 253–254, 311
antigen‑antibody complex 5
antigen generation strategies 45–46
anti‑respiratory syncytial virus (RSV) 50, 228
Application Programming Interface (API) 30
Argo 29
articial intelligence (AI) 14, 16, 17, 345
for antibody design 346–347
challenges and opportunities 356
on construct design 47–48
logic gates, biosensors, and de novo design
355, 356
market trends and commercialization 355
multispecic antibodies 347–350, 348, 349, 351,
352–353, 354
NGS‑enabled in silico antibody discovery
68–70
on protein production 47–48
on target protein expression 47–48
articial intelligence and machine learning (AIML)
3, 4, 7, 8, 60, 68–70
articial neural network (ANN) 94
atomic contacts 143
automation
laboratory 19–25
physical 21
in synthetic biology 20, 20 –21
technologies and characteristics 21, 22
avidity 162
Baculovirus particle (BVP) 230, 231, 244, 245
Bayesian optimization (BO) 140, 141, 353
B‑cell epitope prediction 50
Beckman Coulter Echo 650 24
bidirectional encoder representations from
transformers (BERT) 96, 102, 184
bidirectional long short‑term memory (LSTM)
47, 87
Biobase 32, 33, 33
biopharmaceutical informatics, strategic vision of 2
BioPhi 92, 93, 100, 103, 129
bispecic antibody (BsAb) formats 142, 245, 333
bispecic IgG antibodies 144–145
bispecic T‑cell engagers (BiTEs) 143
BLOcks SUbstitution Matrix (BLOSUM) 97, 269
bone marrow plasma cells (BMPCs) 65
broadly neutralizing antibodies electronic resource
(bNAber) 167, 168
CamSol 49, 94
C5a receptor 48
CAR‑T therapy 328–331, 329, 332, 333
CD3 bispecic antibody (bsAb) 333–334, 334
CDR‑H3 85, 87, 90, 123, 127, 135, 145, 264–265, 269
CDRs see complementarity determining regions
(CDRs)
cellular immunotherapy 316
channelrhodopsins (ChR) 48
ChatGPT 25
chemistry, manufacturing, and control (CMC) 254
Chinese Hamster Ovary (CHO) 46, 123, 230, 293
classical approach 256–264, 257–259, 261, 262
classical molecular simulations (MD) simulations
184, 206–207
Claudin 6 (CLDN6) 44, 45
Claudin 9 (CLDN9) 44, 44
clinical antibody data set 232, 232–234, 233, 234
clone rescue method 68
clone self‑interaction assay using bio‑layer
interferometry (CSI‑BLI) 230
cloud computing 25–30
clustered regularly interspaced short palindromic
repeats (CRISPR) 11
CNTO607 antibody 230
coarse‑grain (CG) 208–210
codon optimization 47
community cloud 26
complementarity determining regions (CDRs) 51, 62,
80, 95, 100, 102, 103, 134–135, 162, 180
computational 2, 2–4, 23
antibody discovery 46–50
multistate design 144–145
protein modeling 125
to ols 116 –117
computer‑aided antibody repositioning 141–142
contemporary approach 264–270, 265
Continuous Bag of Words architecture 99
convolutional neural network (CNN) 70, 118, 269, 272
Coronavirus disease 2019 (Covid‑19) 1, 34
Covid‑19 Open Research Dataset (CORD‑19)
Search 34
Crigler‑Najjar syndrome type 1 (CN1) 335
critical quality attributes (CQAs) 261, 296
cross‑interaction chromatography (CIC) 229
cryogenic electron microscopy (Cryo‑EM) 49,
134,202
curated protein aggregation database (CPAD) 2.0 168
cyber‑physical systems (CPS) 300
DAbI see discovery of antibodies in silico (DAbI)
data availability 32, 33, 78, 291, 309
databases
with domain‑specic information 84
in machine learning approaches 79–80
Index 363
mixed 84
public antibody 78–79
sequence80
structural 80
Databricks 29
data‑driven approach 30
data‑generation methods 19–25
data governance18
data integrity 18, 62
data quality 17–18
data representation 97, 98, 120
DBTL see Design‑Build‑Test‑Learn (DBTL) cycle
DeepAb 69, 80, 85, 87, 183
DeepCell Kiosk 30
deep generative models 136
antibody generation 119–121, 121, 122–124, 123
large language models 122
libraries derived from 268–270
DeepH3 85, 86, 87
deep learning (DL) 68, 117–118
antibody‑binding prediction by 87–90
antibody structure prediction with 84–87,
85–86
applications 95–96
classication 118
deep mutational scanning (DMS) 135, 140, 182
deep neural networks 14, 49, 96, 118, 266, 268
de novo discovery campaigns 237–240, 238, 239,
240, 241
Design‑Build‑Test‑Learn (DBTL) cycle 19, 21, 31
developability 3, 7–8, 90–96, 104, 117, 228
chemical liabilities assessment 241–243, 242
clinical antibody data set 232, 232–234, 233, 234
control antibodies 235–237, 236
dened 255
de novo discovery campaigns 237–240, 238,
239, 240, 241
discovery process 229
engineered libraries 264–270, 265
history 230–232
human B‑cell‑derived antibodies 234–235, 235
prediction and optimization 352
developability index 94
DevOps 30
digestion‑ligation method 21
digital transformation 3, 7, 12–13
case studies 31–34
chemistry, manufacturing, and controls data
32–34
cloud computing 25–27
data‑driven approach 30
data‑generation methods 19–25
developments 19–30
laboratory automation 19–25, 20, 22
machine learning 27–30, 28
ML Operation 27–30
of pre‑clinical R&D
barriers to automation adoption 18–19
cultural challenges 16–17
data governance18
data integrity 18
data quality standards 17–18
operational challenges 15–16
DiscoTop e 3.0 50
Discovery of antibodies in silico (DAbI) 4
classical antibody discovery 6
conceptual workow 4, 4–5
disease‑scale platform models 310
diverse hit identication libraries 267–270
DLAB 142, 272
DNA assembly 21
docking 5, 49, 89, 130, 141–142, 163, 168–169
drug discovery
biotherapeutic 8
cloud computing in 25
computational workow in antibody 117
Design‑Build‑Test‑Learn (DBTL) cycle 28
machine learning in 27
systems biology 281, 282, 285–287, 286
drug repositioning 14, 141–142
drug target identication and validation 287–292,
288, 289–292
efcacy 320
Electronic Laboratory Notebooks (ELN) 26
embedding 69, 86, 94, 98–103, 120, 121, 177, 255
emerging approach 270–272, 271
ENPICOM 62, 63
Ensembl 43
EpiPred 50, 169
epitope 45, 50
prediction 88–89
Escherichia coli 45, 47
evolutionary scale modeling 1b (ESM‑1b) 101
experimentation 2, 2, 3
extrapolation 14
FACS see uorescence‑activated cell sorting (FACS)
FAIR (ndable, accessible, interoperable, and
reusable) data management 3, 16, 17, 31
Fc receptors (FcRn) 143, 231, 233, 234, 273,
312–314
uorescence‑activated cell sorting (FACS) 65, 66,139
Food and Drug Administration (FDA) 12, 13, 162,
229, 293
ALCOA and ALCOA plus principles 18
Technology Modernization Action Plan 13
force eld (FF) 184
14th Critical Assessment of Protein Structure
Prediction (CASP14) 345
FreeSASA 69
364 Index
GANs see generative adversarial networks (GANs)
GenBank 79
Geneious Biologics 62, 63
gene ontology functional analyses 285
generative adversarial networks (GANs) 70, 79, 89,
95, 103
generative AI 16
generative models 69, 70, 116
Generative Pre‑trained Transformer 3 (GPT‑3) 25
gene set enrichment analysis (GSEA) 285
gene therapy 311, 318 –319
genome‑scale metabolic ux balance models 287
Gibson assembly 21
Golden Gate assembly 21
“gold‑plated” approach 261
Google Kubernetes Engine 30
G protein‑coupled receptors (GPCRs) 43, 45, 48
graphics processing units (GPUs) 27
Haplosaurus 43
heavy chain complementarity determining region 3
(HCDR3) 62, 67, 68, 70
HEX 141, 212
high viscosity index (HVI) 94
homology modelling 48
Hu‑mab 91
human B‑cell‑derived antibodies 234–235, 235
Human Genome Project 11
human immunodeciency viruses (HIV) 163
human interaction 19, 20
humanization methods 91, 92
humanness scores 91, 92, 93, 102
hybrid cloud 26
hybridomas 3, 7, 41, 45, 64
IaaS see infrastructure as a service (IaaS)
IgBLAST 62, 62
IgFold 69, 183
IgG BsAbs 143–144
Illumina platform 61
IMGT High‑VQUEST 62, 63
IMGT/V‑QUEST 62, 62
immune and naïve libraries 267
immune repertoire analysis 119
Immune2vec 100, 100
immunization 3, 7, 41, 45, 46, 59, 65, 66
ImMunoGeneTics information system (IMGT) 165
immunogenicity 91–93
immunoglobulins (IGs) 161–162
immunotherapeutic agents 143
improved positional frequencies 91, 92
®
IncuCyte
23
iniximab 323–324
infrastructure as a service (IaaS) 26, 27, 29
in silico antibody discovery, next‑generation
sequencing‑enabled 68–70, 70
in silico generation, of antibody sequences 4–5
integral membrane proteins 45–46
Integrated Nanobody Database for
Immunoinformatics 79
interfacial contacts (ICs) 178
interleukin‑4 (IL‑4) 51
International ImMunoGeneTics Information
System (IMGT) 80
international nonproprietary name (INN) 230
interpolation 14
in vitro antibody discovery 66–68, 67
in vitro to in vivo correlation (IVIVC) 331, 332, 333
in vivo antibody discovery 64–66, 65
Ion Torrent 61, 62
ISLAND (In SiLico protein AfNity preDictor) 177
k‑nearest neighbors (KNN) regression model
102,129
Kubeow 30
Kubernetes (K8s) 26, 27, 29, 30
LabGenius 23, 25, 31
laboratory automation 19–25, 20, 22
Laboratory Information Management Systems
(LIMS) 26, 32, 33
labor‑intensive processes 130
language models (LMs) 182–184, 183
large language models (LLMs) 14, 25, 116, 119,
122, 345, 345–346
lentiviruses 319
LIMS see Laboratory Information Management
Systems (LIMS)
linear discriminant analysis (LDA) models 139
Linux 26
lipid nanoparticle (LNP) delivery system 321
liquid handling robots 21, 23
logistic regression (LR) 139
long short‑term memory (LSTM) 345
low‑density lipoprotein (LDL) 253
LSTM‑recurrent neural network (RNN) 90
mAbs see monoclonal antibodies (mAbs)
Ma bTope 50
machine learning (ML) 19, 27–30, 28, 60, 116
for antibody design 346–347
in antibody discovery 84–96
antibody‑binding prediction by deep
learning 87–90
developability 90–96
structure prediction with deep learning
84–87
challenges and opportunities 356
on construct design 47–48
databases in 79–80
data collection phase in 25
in Design‑Build‑Test‑Learn (DBTL) cycle
27,28
Index 365
functionality of antibodies 136, 137–138,
139 –141
integration 26, 117
life cycle in drug discovery 28, 28
logic gates, biosensors, and de novo design
355, 356
market trends and commercialization 355
for multi‑spanning membrane protein 48
multispecic antibodies 347–350, 348, 349, 351,
352–353, 354
protein engineering platform 31, 32
on protein production 47–48
protein sequence design 127
on target protein expression 47–48
macromolecular crystallography (MX) 201–202
masked language modeling (MLM) 93, 129
mathematical models 285–287, 286, 289, 291, 295,
295, 298, 298
mean absolute error (MAE) 180
mechanistic models 286, 291
message‑passing neural network (MPNN) 126
Meta’s Evolutionary Scale Modeling (ESM)
model345
microuidic devices 21, 24
MiXCR 62, 63
mixed databases 84
ML see machine learning (ML)
ML Operation (MLOps) 27–30
modied probabilistic neural network (MPNN) 272
molecular dynamics (MD) simulation 93, 202–205,
204
molecular‑level analyses 285
molecular mechanic Poisson‑Boltzmann surface
area (MMPBSA) method 219
molecular mechanism of action (MMOA) 288
molecular simulations 118, 201
aggregation‑prone antibody therapeutics
220–221
all‑atom vs. coarse‑grain 208–210
in antibody drug development 210 –216
binding mechanism and oligomeric
preference217, 218, 219
challenges 206–207
constant domains 208
energetic interactions 219–220
molecular dynamics simulations 202–205, 204
Monte Carlo simulation 205–206
periodic boundary conditions 207–208
monoclonal antibodies (mAbs) 40, 65, 77, 78, 94
proper ties 214–215
salt and pH 211–213, 212
tandem experimental and simulation studies
215–216
th erap eutic target 311–314
Monte Carlo (MC) simulation 205–206
mouse myeloma (NS0) cells 293
MPEPE (mutation predictor for enhanced protein
expression) 47
mRNA 47, 320–321, 335, 338
MsAbs see multispecic antibodies (MsAbs)
multi‑objective Bayesian optimisation (MOBO).
31, 32
multiple instance learning (MIL) 102
multiple sequence alignments (MSAs) 95, 271
multiplicative LSTM (mLSTM) 345
multi‑spanning membrane proteins 46
multispecic antibodies (MsAbs) 344–345,
347–350, 348, 349, 351
developability prediction and optimization 352
in silico modeling and simulation 353, 354
structure prediction and modeling 352
virtual screening and lead identication
352–353
multispecic biologics
antibody formats 142–14 4
computational multistate design 144–145
multistate design (MSD) 133, 144–145
multivariate Gaussian (MG) statistical score 91, 92
mutation prediction methods 179–180
mutual population shift 217
NanoNet 85, 87
natural language processing (NLP) 99, 182–184, 183
neural message‑passing architecture 88
new molecular entities (NMEs) 344–345
Nextow 289
next‑generation sequencing (NGS) 20, 24, 59
for antibody discovery
data analysis 60–64, 62, 63
in silico 68–70, 70
in vitro 66–68, 67
in vivo 64–66, 65
tools 60–64, 62
antibody repertoire metrics 63
deep sequencing of antibody repertoires 60
N‑glycosylation 296
NGS see next‑generation sequencing (NGS)
noninteracting surface (NIS) 178
non‑linear mixed‑effects (NLME) 309
nuclease‑directed integration system 96
OASis 92, 93
observed antibody space (OAS) 68, 79–80, 165
omics methods 283, 285
OpenRiskNet 30
OpenStack 26
ordinary differential equations (ODEs) 291, 337
Orientations of Proteins in Membranes (OPM) 43
PaaS see platform as a service (PaaS)
Pachyderm 28, 29
366 Index
Pacic Biosciences (PacBio) 61, 62
paired sequencing 60
palivizumab 51, 228, 229
Pan Immune Repertoire Database (PIRD) 165
Parapred 88, 102
paratope, prediction 88
Patch D ock 141
PCSK9 inhibitory monoclonal antibody 253
pharmacokinetic/pharmacodynamic (PK/PD)
models 307
antibody‑drug conjugates 314–316
application 323–324
biot herap eutics 310–311
CAR‑T therapy 331, 332, 333
cell therapies 316–318, 317
gene therapy 318 –319
modeling and simulation approaches 321–323
monoclonal a ntibo dies 311–314
mRNA/siRNA/oligonucleotide therapeutics
320–321
potential drawbacks 309
principles 308
‘top‑down’ approach 308–309
vaccine 319–320
quantitative systems pharmacology models 308
physical automation 21
physiologically based pharmacokinetic (PBPK)
model 310, 326–328, 327
PipeBio 62, 63
pipetting assistant robots 214
PK/PD models see pharmacokinetic/
pharmacodynamic (PK/PD) models
platform as a service (PaaS) 26, 29
polymerase chain reaction (PCR) 24
polyspecicity reagent (PSR) 230, 238, 266
Positioning of Proteins in Membranes (PPM2.0 and
PPM3.0) 43
position‑specic scoring matrix (PSSM) 181
post‑fusion 52
post‑translational modications (PTMs) 43,
181–182
PPI‑Afnity 178
pre‑clinical R&D
challenges in digital transformation 15–19
current state of digitalisation in 14–15
pre‑fusion 52
primary databases 165, 166–167, 168
private cloud 26
Pr oAf MuSeq 180
process analytical technologies (PATs) 300
PROPKA3 69
ProtBERT 100, 101, 102
protein
antigens 45
haplotypes 43
integral membrane 45–46
RSV F 51–52
stability 128
targets 41, 41–45, 43
thermodynamic stability 128
PROtein binDIng enerGY prediction (PRODIGY)
178
Protein Data Bank (PDB) 43, 80, 165, 206, 271
protein engineering 31, 116, 117, 119, 127–129
ProteinMPNN 47, 126
Protein‑Protein Afnity Predictor (PPA‑Pred) 177
Protein–Protein Interaction Prediction (PIPR) 177
protein‑protein interactions 171, 172 –176, 210 –211
Protein Structural Bioinformatics Overview
(PreStO) 43
proteomics 24, 120, 287, 289, 291
ProTherm 95
PROXiMATE databases 167, 168
public antibody databases 78–79
public cloud 26, 30
PyIgClassify 80, 86, 87
Python 26
PyTorch 28, 29
quantitative systems pharmacology (QSP) models
178, 307
biot herap eutics 310–311
CD3 bispecic antibodies 333–334, 334
clinical translation and optimization 324,
325,326
computational power 309
disease‑scale platform models 310
drug‑specic parameters 310
for mRNA therapeutic lipid nanoparticle 335
PBPK models 310
sickle cell disease 335–337, 336
system‑specic parameters 309–310
models308
random‑access memory (RAM) 27
Razor 47
receptor‑binding domain (RBD) 51
recombinant DNA (rDNA) technology 293
RECON 133
recurrent convolutional neural network
(RCNN)177
recurrent neural networks (RNNs) 118, 345
red blood cells (RBCs) 335–337
representation learning 98
ResDom 43
research and development (R&D) 12
respiratory syncytial virus (RSV) 50
RESP model 139, 177
rigid‑body docking programs 141, 142
RosettaAntibodyDesign (RAbD) 135
RoseTTAFold 49, 183
Index 367
SAAMBE‑SEQ 174 , 175, 179, 180
SaaS see software as a service (SaaS)
SAbDab 68, 80, 83, 86, 165, 166
sampling 125–127
Sanger sequencing 60–61, 66
Sapiens 92, 93, 103
SARS‑CoV‑2 50, 134–136
SARS‑CoV‑2‑specic antibody 70
ScanNet 88
SciKit‑Learn 29
scoring 126–127
Seldon 30
self‑attention 101, 102
sequence databases 79, 80
shape complementarity 133
short‑read sequencing 68
sickle cell disease (SCD) 335–337, 336
single‑chain variable fragment (scFv) 94
single‑nucleotide polymorphisms (SNPs) 43
SKEMPI 2.0 136, 168
Skip‑Gram 99
small interfering RNAs (siRNAs) 321
SoDoPE (soluble domain for protein expression) 47
software as a service (SaaS) 26, 30
solanezumab 217, 218, 219
solubility, prediction of 93–95
Solubis 94
soluble cytosolic protein (SCP) 230
soluble membrane protein (SMP) 230
somatic hypermutation (SHM) 63, 63, 130, 162
specialized databases 168
specic‑sequence‑optimization variants 256–264,
257–259, 261, 262
splice variants 43
STACKn 27, 30
stamulumab 254
standardisation, of data formats 24
structural databases 51, 80
structure‑based simulations
classical ML 125
complex molecular architecture 125
computational protein modeling 125
computer‑aided antibody repositioning 141–142
data resources 125
functionality of antibodies 130, 131–132,
133–135
machine learning 127
sampling 125–127
scoring 126–127
stability of antibodies 127–129
structure prediction, antibody 84–87, 85–86
synthetic and semi‑synthetic libraries 267–268
synthetic biology 11, 19
automation in 20, 20–21, 24
systems biology 281–282
big data approach 298–299, 299
components 282, 282
computational methods 285–287, 286
drug target identication and validation
287–292, 288, 289–292
experimental methods 282–283, 283, 284, 285
growth and productivity 292–295, 293,
294–295
product quality attributes 296, 297–298, 298
target‑mediated drug disposition (TMDD) 311,
312, 314
target proteins
for antibody discovery 41, 41–45, 43
bioinformatic analysis 42–43
computational structure prediction of 48–50, 49
impact of AI/ML on 47–48
Tcel l Match 172, 177
T‑cell receptors (TCRs) 84, 143, 165, 219
t‑distributed stochastic neighbor embedding
(t‑SNE) 10 0
Tecan Cellerity 23
Tecan Freedom EVO 23
Technology Modernization Action Plan 13
TensorFlow 28–30
therapeutic antibody proler (TAP) 95
Thera‑SAbDab 68, 83, 167, 168
thermostability, prediction of 93–95
TIsigner web service47
TMDD see target‑mediated drug disposition
(TMDD)
transformer architectures 101
transformer‑based protein language models 140
two‑layer neural networks 139
type I membrane proteins 46
Uniform Manifold Approximation and Projection
(UMAP) 102
UniProt 43
unique molecular identiers (UMIs) 62
vaccine 50, 51, 120, 319–320
variable heavy chain (VH) 91
variable light chain (VL) 91
variational auto encoders (VAEs) 103, 270
Vertex AI (Google) 25
virtual machine images (VMIs) 26, 29
virtual machines (VMs) 25, 27
virus‑like particles (VLPs) 46
viscosity, prediction of 93–95
ZDOCK 49, 89, 141, 169, 170, 272