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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5435_Библиотеки_им_академика_М_И_Перельмана
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7. GALAHAD: GALAHAD allows researchers t o automatically develop pharmaco-
phore hypotheses and structural alignments from a set of molecules that bind at
a common site.
8. GASP: GASP performs pharmacophore elucidation without requiring prior knowl-
edge of pharmacophore elements or constraints.
9. Tuplets: Tuplets facilitate the retrieval of compounds from molecular structure
databases that are likely to exhibit biological activity. Additionally, Tuplets can be
used to prioritize virtual combinatorial libraries for pharmaceutical research and
can provide a biasing descriptor to allow the design of focused combinatorial
libraries.
10. DISCOtech: DISCOtech performs pharmacophore elucidation from a set of active
compounds. Starting from a set of representative conformers for each molecule,
DISCOtech considers all possible mappingsoffeaturestocreateasetofalign-
ments, each of which is a hypothesis for the pharmacophore and its geometry.
11. Surflex-Dock: Surflex-Dock offers unparalleled enrichments in virtual high-
throughput screening combined with state-of-the-art speed, accuracy, and
usability.
12. CScore: CScore uses multiple types of scoring functions to rank the affinity of li-
gands bound to the active site of a receptor.
13. EA-Inventor: EA-Inventor is a new and different approach to de novo design. It
enables researchers to invent new compounds, new R-groups around a fixed scaf-
fold, or new scaffolds.
14. LeapFrog: LeapFrog uses a receptor site or a CoMFA model as the b asis for de
novo ligand design, and can be used to optimize lead compounds or to generate
novel structures.
15. RACHEL: RACHEL performs automated combinatorial optimization of lead com-
pounds by systemically derivatizing user-defined sites on the ligand.
16. Biopolymer: Biopolymer delivers an extensive set of tools for building , predict-
ing, visualizing, and manipulating the 3D structure of proteins, peptides, nucleic
acids, and polysaccharides.
17. ProTable: ProTable uses SYBYL’s molecular spreadsheet to analyze and evaluate
protein struct ures. ProTable creates Ramachandran plots, assesses deviation of
local geometries and side-chain rotameric states from standard protein values,
and determines the energetic and structural properties of each residue.
18. SiteID: SiteID provides analysis and visualization tools to identify potential bind-
ing sites within or at the surface of macromolecules.
19. Composer: Composer builds 3D models of proteins from sequence using knowl-
edge-based homology modeling methods. For a given protein sequence, the com-
poser searches a database of known structures to find homologous proteins.
Composer aligns regions of the sequences that have topological equivalence and
completes construction of a 3D model by adding side chains using rule-based sub-
stitution tables and suitable loop fragments of high-resolution protein structures.
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20. FUGUE: FUGUE recognizes distant structural homologs of a target sequence by se-
quence-structure comparison. It assesses the compatibility between a target sequence
and structural profiles of all known protein structural families. The key elements of
FUGUE are environment-specific substitution tables, structure-dependent gap penal-
ties, automated alignment method selection.
21. GeneFold: GeneFold identifies a protein’s function from its amino acid sequence.
Sequence homology and threading methods are used to recognize protein folding
patterns. GeneFold threads a new sequence through known protein structures
and calculates how well the sequence matches.
22. MatchM aker: MatchMaker uses an inverse-folding method to predict the 3D
structure of a protein from its amino acid sequence. By comparing a new protein
sequence to its topology fingerprint database, MatchMaker assesses the ability of
a sequence to adopt characteristic topologies.
23. Legion/CombiLib Maker: Legion and CombiLib Maker provide the capability for
building and storing combinatorial libraries of compounds. Libraries of com-
pounds can be defined and enumerated with full control of stereochemistry.
24. OptDesign: Starting from a virtual combinatorial library (cSLN file), OptDesign
selects compounds that balance the practical constraints of combinatorial library
design, such as cost and ease of synthesis, with diversity and representativeness,
thereby performing true double-objective optimization.
25. Selector: Selector characterizes, compares, and samples sets of compounds. The
selector can create diverse or representative subsets, filter compound lists based
on properties, find compounds similar to a lead compound, and compare the di-
versity of sets of compounds.
26. Diverse Solutions: Diverse Solutions assesses the chemical diversity of a popula-
tion of molecules, selects diverse or representative subsets, and compares the di-
versity of two or more different populations of molecules.
27. UNITY: UNITY is a search and analysis system for exploring chemical and biologi-
cal databases. UNITY’s 2D searching capabilities offer exact, substructure, and
similarity searching.
28. Concord: Concord sets the industry standard for extremely rapid conversion of
2D (or crude 3D) input to accurate, geometry-optimized 3D structures.
29. Confort: Confort is a powerful conformational analysis tool that performs ex-
haustive yet rapid analysis of drug-sized molecules. It can be used to identify the
global minimum energy conformer, all local minima within a user-specified en-
ergy range, or a maximally diverse subset of conformers.
30. ProtoPlex: ProtoPlex provides a mechanism for making accurate and realistic
structural representations of chemical compounds. ProtoPlex allows users to con-
trol the protonation, deprotonation, or tautomerization for each chemical class of
proto-centers.
31. MM3: MM3 is one of the most highly respected, peer-reviewed software systems
available for performing molecular mechanics calculations.
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32. MM4: MM4 is the latest in a well-known series of molecular mechanics programs
for generating high-quality geometries and energies for calculating small mole-
cule structures. MM4 offers increased accuracy in calculating molecular struc-
tures, performing conformational analyses, and computing spectroscopic and
thermodynamic properties.
33. StereoPlex: StereoPlex generates multiple stereoisomers of each input compound
structure according to a user-specified limit on the number of stereoisomers and
a user-specified priority rule, which tells the program which stereoisomers to
generate if the complete set would exceed the user’s limit.
34. AMPAC: Rapidly calculate the transition states and spectral properties using
semiempirical quantum mechanics.
35. HiVol: HiVol works within the SYBYL environment to facilitate the exploration of
a high v olume of data such as that generated by high-throughput synthesis or
screening.
36. GSSI: GSSI is a novel, general approach to modeling solution-ph ase properties
through a fairly rigorous yet efficient consideration of solute-solvent interactions.
It is useful for predicting various partition coefficients and membrane permeabil-
ity coefficients in support of ADME-related efforts.
37. HSCF: HSCF is a unique semiempirical molecular orbital (MO) “information server.”
38. Hint: Hint provides tools to visualize and calculate the relative strengths of non-
covalent interactions between and within biological molecules. It calculates 3D
hydropathic fields and 3D hydropathic interaction maps and estimates logP for
modeled molecules or data files.
39. ZAP: ZAP uses the Poisson-Boltzmann equation to calculate the electrostatic po-
tential surrounding a molecule in a medium of varying dielectric.
VLifeMDS: VLife MDS is a comprehensive and integrated software package for com-
puter-aided drug and molecular discovery. With its flexible architecture, VLifeMDS is
ready to meet demands from a structure-based design approach as well as a ligand-based
design approach. VLife’s offerings have a range of applications in life sciences and allied
sectors, including pharmaceutical, biotechnology, agri-biotechnology, chemical, petro-
chemical, cosmetics, nutraceutical, and healthcare.
VLife’s includes the following drug design software:
a. VLife Molecular Design Suite (VLife MDSTM): A comprehensive integrated
suite of discovery products with modular functionalities for molecular modeling,
simulation, analysis, visualization, interpretation, and prediction.
b. QSARPro TM: Specialty software for QSAR with multiple methods for variable se-
lection, statistical regression, and visualization.
c. ChemXplor TM: Software focused on chemoinformatics database generation and
database search based on molecular structures, descriptors, fingerprints.
d. BioPredicta TM: Specialty software for protein modeling and interaction studies.
e. VLife Base: For molecule drawing, visualization, and analysis.
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f. VLife Engine: For all molecular operations and key activity of conformer
generation.
g. ProModel: For homology modeling with options for manual and automated tem-
plate-based modeling.
h. VLifeDock: For docking st udies with multiple scoring functions and options for
rapid and high precision docking.
i. GQSAR: To obtain site-specific clues for new molecule design and optimization.
j. VLifeQSAR: For 2D-/3D-QSAR with multiple variable selection options and regres-
sions methods.
k. VLife Auto QSAR: For an automated workflow of the entire QSAR modeling and
a consensus-based result on the best model.
l. VLife SCOPE: To optimize the lead design based on the interaction between the
molecule and interacting residues in the active site.
m. ChemDBS: For exploring databases with multiple search options including prop-
erty, fingerprint, and pharmacophore-based.
n. MolSign: For pharmacophore generation and application in optimization and
searches.
o. ProViz: For calculating and visualizing molecular properties on the surface of a
molecule.
p. LeadGrow: For creating a combinatorial library with a choice of substitutions.
q. LeadGrow+: An extension to the combinatorial library generation capability that
ensures that molecules that can be synthesized can only be generated in a library.
r. ConfAlys: Generate conformers systematically or with the Monte Carlo method.
Get the results in the worksheet with row, column, and function-based operations
(plot, comparison, correlation, etc.).
s. MolBuild (Engine): Draw the molecular structures and visualize molecules in
various models, colors, scales, and orientations.
t. BioPredicta: Construct proteins/poly peptides/DNA/RNA just by choosing amino/
nucleic acid residues and secondary structure. Edit protein structures using muta-
tion, insertion, deletion, excising, joining, the renumbering of residues, etc.
u. CombiLib: Generate combinatorial libraries of molecules with structural tem-
plates. Construct and save your templates and substituents for further use. Choose
functional groups for substitution at each site.
v. Chem Phore: Pharmacophore identification is carried out. Enhance your
searches using receptor or pharmacophore-based queries. Identify the pharmaco-
phoric points for an active site of the receptor just by picking.
BioSuite: BioSuite is a versatile, comprehensive, portable, scalable software suite, ca-
tering to the needs of scientists and academicians and the bioinformatics industry.
The BioSuite package is divided into four main modules: genomics, protein modeling
and structural analysis, simulations, and drug design. This software suite has been de-
veloped in such a way that it can be used for genome analysis, sequence analysis, 3D
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modeling, simulation, manipulation, structural changes, drug design, pathway model-
ing, SNP analysis, and comparative genomics. The suite runs on platforms such as
Compaq, IBM Aix, SGI, Linux clusters, and the operating systems of Unix and Linux
flavors, the official said. The 3D structure manipulations in the module will help in
building molecules, side-chain placements, and stereochemical editing and the drug
design model will help in developing de novo designs with high-throughput screening
of chemical databases, the official claimed (Table 6.6).
6.1.3 Bioinformatics software and databases
Bioinformatics is an interdisciplinary approach, which includes the data related to ge-
nomics, proteomics, transcriptomics, population genetics, and molecular phyloge-
netics (Figure 6.12). Genomic analysis helps in repurposing the existing drugs against
other pathogens. Transcriptomic data are used to identify the differentially regulated
gene [56]. Several software, databases and web services related to drug discovery are
available at http://click2drug.org. It is maintained by Swiss Institute of Bioinformatics.
These are categorized into: (a) databases, (b) chemical structure representations, (c)
molecular modeling and simulation, (d) homology modeling, (e) binding site pred ic-
tion, (f) docking, (g) screening for drug candidates, (h) drug target prediction, (i) li-
gand design, (j) binding free energy estimation, (k) QSAR, and (l) ADMET. Several
software tools are available freely including ChEMBL, UCSF Chimera, Swiss Similarity,
SwissBioisostere, SwissTargetPrediction and SwissSideChain, CHARMM, and PyMOL.
UCSF Chimera is not only a 3D visualization tool but also a platform for software de-
Table 6.6: List of drug design software packages.
S. no. Name of
software
Types of software Application
. Insight-II, DiscoveryStudio Structure-based Graphical molecular modeling and de
novo drug design
. BioSsuite Structure-based Genomics, protein modeling, structural
analysis, simulation, and drug design
. Phase, Glide, Liaison Ligand-based Pharmacophore modeling, Ligand
receptor docking
. Rachel, GALAHAD, HQSAR Ligand-based and
receptor-based design
Computational informatics software
for drug discovery
. Sanjeevini Structure-based Active site-directed drug design
. VLife Auto QSAR Structure-based as well as
a ligand-based design
Pharmacophore generation and
homology modeling
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velopers in structural biology. SwissSimilarity is used for virtual screening. SwissBioi-
sostere is applied for ligand design. SwissTargetPredicti on and SwissSideChain are
used to facilitate experiments that expand the protein repertoire by introducing non-
natural amino acids. SwissDock is used for docking drug candidates (small molecules)
on proteins.
6.1.4 Role of bioinformatics in drug design and discovery process
Bioinformatics tools help in accelerating the identification of the drug target and
screening of drug candidates [57]. This technique also facilitates the characterization
and prediction of side effects and resistance of drugs respectively. In the drug discov-
ery process, the bioinformatics tool utilizes high-throughput molecular data in com-
parison between symptom carriers (patients, animal disease models, cancer cell lines,
etc.) and normal controls (Figure 6.13).
Bioinformatics play a vital role in accelerating the desirable drug target identifica-
tion. It also helps in screening and refinement of drug candidates. These tools facilitate
the characterization of side effects and also predict drug resistance. For optimization
study, several high-throughput data are considered such as genomic, epigenetic, ge-
nome architecture, cistromic, transcriptomic, proteomic, population genetics, molecular
phylogenetics, whole genome sequencing, and ribosome profiling data [58]. Genomic
analysis can help in repurposing existing drugs against other pathogens. Genomic
and whole exome sequencing of patients with inherited disorders have recovered
many somatic mutations, which are associated with genetic disease [59]. B ioinfor-
matics focuses on three approaches to check if the mutation has major impact on
gene function: (i) whether the mutation replaces an amino acid by a very different
one (e.g., nonpolar uncharged glycine by a positively charged arginine) at a typically
conserved site, (ii) whether the mutation occurs in a highly conserved noncoding se-
quence (which is typically done by comparing genomes between human and non-
Genomics
Transcriptomics
Proteomics
Metabolomics
Desired Result
Softwares/Tools
Databases
Bioinformatics
Figure 6.12: Bioinformatics database.
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human primates.), and (iii) whether the mutation occurs in a known signal (e.g., reg-
ulatory motif, splice sites, transcription initiation and termination sites) for cellular
machinery (e.g., ribosome, spliceosome, degradosome) (Figure 6.14). Bioinformatic
tools are used to scan genomes for regulatory motifs. Such tools include position
weight matrix (PWM) to find the genomic location of a known motif, Gibbs sampler
for de novo motif discovery and support vector machines (SVMs) that can be used to
extract differences between two groups of sequences (e.g., motif-present and motif-
absent) and to us e the resulting information to detect/scan motifs in genomes [60].
Further, artificial intelligence (AI) plays vital role in the analysis of larger data sets
by statistical machine learning (ML) methods. The advancement of AI-based sophisti-
cated machine learning tools has a significant impact on the drug discovery process
[61]. AI mimics human behavior by simulating the human intelligence by computer
techniques. ML exploits the relationship between a biological activity and chemical
structure during drug design. Different categories of ML include supervised learning
and unsupervised learning. The subcategories of supervised learning are classification,
and regression methods that predicts the model on the basis of input and output data
sources [62]. Supervised ML is applicable to a disease in diagnostic methods, ADMET in
a classification method’s output, and to drug efficacy in regression methods. Different
methods of the ML technique include random forest (RF), SVM, and neural networks
(NNs) work with nonlinear dependence among binding interactions (Figure 6.15) [63].
Target Identification
Protein
Crystallization
X-ray Data
Collection
Protein Structure
Determination
Ligand Design &
Synthesis
Modelling Studies &
Structural Bioinformatics
Protein Engineering
Protein Expression
& Purification
Co-crystallization
Ligand Soaking
Protein:Ligand Complex
Formation
Protein:Ligand Structure
Determination
Lead Optimization
Biochemical &
Biophysical Assays
Figure 6.13: Structural bioinformatics.
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Genomic
Mutation
Gene duplication
Gene regulation networks
Genome rearrangement
High-throughput data
used in Bioinformatics
Cistromic
Genome wise binding sites of proteins
(Transcription factors)
Somatic mutation in coading regions
Genome compartmentization
Whole genome
sequencing
Genome architecture
Ribosome profiling
Transcriptomic
Epigentic
Proteomic
Post-translational modification
Cellular localization
DNA methylation
Histone modification
Differential gene regulation
Somatic mutation
Translation regulation
Translation initiation
Translation elongation
Translation termination
Figure 6.14: Major types of high-throughput data and their key information in bioinformatics.
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The integration of ML algorithms helps in discovering new chemical entities by analyz-
ing, learning, and explaining pharmaceutical big data [64]. SYNSIGHT has introduced
an AI-based integrated platform in combination with VS and molecular modeling to cre-
ate huge biological models for drug development (Table 6.7). Hence, many leading phar-
maceutical compani es are col laborating to integrate AI and ML methods with their
drug discovery pipelines [65]. Since December 2016, Pfizer has been collaborating with
IBM to take advantage of their multicloud platform Watson for immuno-oncology drug
discovery process. Similarly, a UK-based company (Exscientia Ltd.) focuses on AI-driven
drug design by collaborating with Sanofi to find therapeutics for metabolic disorders.
Recently, Exscientia announced a success story in collaboration with GlaxoSmithKline
(GSK), where they claimed the discovery of a highly potent lead molecule for the treat-
Molecular data
Clinical data
Biological
modeling
Integrative
bioinformatics
Scientific
literature
Analytical in
silico techniques
Pharmacological
data
Figure 6.15: Integration of AI and bioinformatics.
ML Algoritthms
Drug Design
Gene Finding
Biology
Data Science
Proein Structure
Prediction
Proein Structure
Allignment
Sequence
Allignment
Pattern
Recognition
Data Mining
Bioinformatics
Visualization
Figure 6.16: Bioinformatics and AI tools.
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Table 6.7: List of computer software and programs used during new drug development process.
Parameters Software Application
Pharmacokinetic parameters DDDPlus Dissolution and disintegration study
GastroPlus In vitro and in vivo correlation for different
formulations
MapCheck Compare dose or fluency measurement
Ligand interactions and
molecular dynamic
AutoDock Evaluate the ligand–protein interaction
GLIDE Ligand–receptor docking
GOLD Protein–ligand docking
BioSuite Genome analyzing and sequence analyzing
Molecular modeling and
structure–activity relationship
Maestro Molecular modeling analysis
ArgusLab Molecular docking calculations and molecular
modeling package
GRAMM Protein–protein docking and protein–ligand
docking
SYBYL-X Suite Molecular modeling and ligand based design
Sanjeevini Predict protein-ligand binding affinity
PASS Create and analysis of SAR models
Image analysis and visualizers AMIDE (A Medical Image
Data Examiner)
Medical image analysis in molecular imaging
Discovery Studio
Visualizer
Viewing and analyzing protein data
Imaging Software
Scge-Pro
Cytogenetic and DNA damage analysis
Xenogen Living Image
Software
In vivo imaging display and analysis
Data analysis GeneSpring Identify variation across set of sample and for
correction method in samples
QSARPro Protein–protein interaction study
REST Analysis of gene expression data
Behavioral study Ethowatcher Behavior analysis
MARS (Multimodal
Animal Rotation System)
Animal activity tracking, enzyme activity,
nanoparticle tracking and delivery study
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