The document discusses various topics related to computer-aided drug design (CADD), including:
1) The definitions of drug-likeness, druggability, and the Rule of Five for screening drug-like molecules. The Rule of Five outlines molecular properties important for a drug's absorption and metabolism.
2) Pharmacophore-based and ligand-based virtual screening methods which use the structure of known active ligands to search compound libraries for similar molecules.
3) The role of virtual screening in CADD to select compounds for biological testing from large databases using techniques like structure-based docking and ligand-based similarity searching. Scoring functions are also used to rank compounds.
This lecture outlines the different strategies for finding a fragment hit and the subsequent elaboration strategies used in order to increase potency to develop a lead compound in drug discovery.
This lecture outlines the different strategies for finding a fragment hit and the subsequent elaboration strategies used in order to increase potency to develop a lead compound in drug discovery.
Cheminformatics, concept by kk sahu sirKAUSHAL SAHU
INTRODUCTION
THE NEED FOR CHEMOINFORMATICS
CHEMOINFORMATICS AND DRUG DISCOVERY
HISTORICAL EVOLUTION
BASIC CONCEPTS
Chemistry Space
Molecular Descriptors
High-Throughput Screening
The Similar-Structure, Similar-Property Principle
Graph theory and Chemoinformatics
CHEMOINFORMATICS TASKS
MOLECULAR REPRESENTATIONS
Topological Representations
Geometrical Representations
TYPES OF MOLECULAR DESCRIPTORS
IN SILICO DE NOVO MOLECULAR DESIGN
FREE CHEMISTRY DATABASE
FUTURE
CONCLUSION
REFERENCE
Cadd and molecular modeling for M.PharmShikha Popali
THE CADD IS FOR THE DRUG DEVELOPMENT THE DIFFERENT STRATEGIES ARE MENTIONED LIKE QSAR MOLECULAR DOCKING, THE DIFFERENT DIMNSIONAL FORMS OF QSAR , THE ADVANCE SAR of it.
Cheminformatics, concept by kk sahu sirKAUSHAL SAHU
INTRODUCTION
THE NEED FOR CHEMOINFORMATICS
CHEMOINFORMATICS AND DRUG DISCOVERY
HISTORICAL EVOLUTION
BASIC CONCEPTS
Chemistry Space
Molecular Descriptors
High-Throughput Screening
The Similar-Structure, Similar-Property Principle
Graph theory and Chemoinformatics
CHEMOINFORMATICS TASKS
MOLECULAR REPRESENTATIONS
Topological Representations
Geometrical Representations
TYPES OF MOLECULAR DESCRIPTORS
IN SILICO DE NOVO MOLECULAR DESIGN
FREE CHEMISTRY DATABASE
FUTURE
CONCLUSION
REFERENCE
Cadd and molecular modeling for M.PharmShikha Popali
THE CADD IS FOR THE DRUG DEVELOPMENT THE DIFFERENT STRATEGIES ARE MENTIONED LIKE QSAR MOLECULAR DOCKING, THE DIFFERENT DIMNSIONAL FORMS OF QSAR , THE ADVANCE SAR of it.
In spite of extensive effort by industry and academia to develop new drugs, there are still several diseases that are in need of therapeutic agents and have yet to be developed.
10 years the identification rate of disease-associated targets has been higher than the therapeutics identification rate.
Nevertheless, it is apparent that computational tools provide high hopes that many of the diseases under investigation can be brought under control.
Hey students here i am attaching the powerpoint presenatation on the Receptor/enzyme-interaction and its analysis, Receptor/enzyme cavity size prediction, predicting
the functional components of cavities and the concept regarding the fragment based drug design.
Pharmacophore identification and novel drug designBenittabenny
pharmacophore is a part of a molecular structure that is responsible for a particular biological or pharmacological interaction that it undergoes. This identification leads to the development of designing a new drug.
The screening of chemical libraries is an important step in the drug discovery process. The
existing chemical libraries contain up to millions of compounds. As the screening at such scale
is expensive, the virtual screening is often utilized. There exist several variants of virtual
screening and ligand-based virtual screening is one of them. It utilizes the similarity of screened
chemical compounds to known compounds. Besides the employed similarity measure, another
aspect greatly influencing the performance of ligand-based virtual screening is the chosen
chemical compound representation. In this paper, we introduce a fragment-based
representation of chemical compounds. Our representation utilizes fragments to represent a
compound where each fragment is represented by its physico-chemical descriptors. The
representation is highly parametrizable, especially in the area of physico-chemical descriptors
selection and application. In order to test the performance of our method, we utilized an existing
framework for virtual screening benchmarking. The results show that our method is comparable
to the best existing approaches and on some data sets it outperforms them.
Pharmacophore Mapping and Virtual Screening (Computer aided Drug design)AkshayYadav176
Pharmacophore Mapping and Virtual Screening (Computer aided Drug design)
Concept of pharmacophore, Pharmacophore mapping, Identification of pharmacophore features and pharmacophore modeling, Conformation search used in pharmacophore mapping, Virtual screening.
Computational modelling of drug disposition lalitajoshi9
computational modelling of drug disposition is the integral part of computer aided drug design. different kinds of tools being used in the prediction of drug disposition in human body. This topic in the CADD explains the details about the drug disposition, active transporters and tools.
COMPUTATIONAL MODELING OF DRUG DISPOSITION.pptxPoojaArya34
Computational modelling of drug disposition is the integral part of computer aided drug design. different kinds of tools being used in the prediction of drug disposition in human body. This topic in the CADD explains the details about the drug disposition, active transporters and tools.
Historically, drug discovery has focused almost exclusively on efficacy and selectivity against the biological target.
As a result, nearly half of drug candidates fail at phase II and phase III clinical trials because of undesirable drug pharmacokinetics properties, including absorption, distribution, metabolism, excretion, and toxicity (ADMET).
The pressure to control the escalating cost of new drug development has changed the paradigm since the mid-1990s. To reduce the attrition rate at more expensive later stages, in vitroevaluation of ADMET properties in the early phase of drug discovery has been widely adopted.Many high-throughput in vitro ADMET property screening assays have been developed and applied successfully .
For example, Caco-2 and MDCK cell monolayers are widely used to simulate membrane permeability as an in vitro estimation of in vivo absorption.
These in vitro results have enabled the training of in silico models, which could be applied to predict the ADMET properties of compounds even before they are synthesized.
Coronaviruses are a family of viruses that can cause illnesses such as the common cold, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). In 2019, a new coronavirus was identified as the cause of a disease outbreak that originated in China.
The virus is now known as the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The disease it causes is called coronavirus disease 2019 (COVID-19). In March 2020, the World Health Organization (WHO) declared the COVID-19 outbreak a pandemic.
Public health groups, including the U.S. Centers for Disease Control and Prevention (CDC) and WHO, are monitoring the pandemic and posting updates on their websites. These groups have also issued recommendations for preventing and treating the illness.
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
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Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Acute scrotum is a general term referring to an emergency condition affecting the contents or the wall of the scrotum.
There are a number of conditions that present acutely, predominantly with pain and/or swelling
A careful and detailed history and examination, and in some cases, investigations allow differentiation between these diagnoses. A prompt diagnosis is essential as the patient may require urgent surgical intervention
Testicular torsion refers to twisting of the spermatic cord, causing ischaemia of the testicle.
Testicular torsion results from inadequate fixation of the testis to the tunica vaginalis producing ischemia from reduced arterial inflow and venous outflow obstruction.
The prevalence of testicular torsion in adult patients hospitalized with acute scrotal pain is approximately 25 to 50 percent
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
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New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
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The prostate is an exocrine gland of the male mammalian reproductive system
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1. SARITA MAURYA
CADD ASSIGNMENT UNIT-4
1. What do you mean by drug-likness and druggability? Explain 'rule of five'
for screening of drug like molecules.
2. Explain pharmacophore/ligand based virtual screening of libraries
3. What do you mean by virtual screening? How it is crucial in CADD?
4. What is docking? Explain rigid and flexible docking with the help of
suitble example
5. What is 'conformational search space', explain it with respect to the
docking.
6. What is the concept behied random searching, explain Tabu search and Monte-
calrlo search, how they differ with each other?
7. What do you mean by systematic searching? Explain 'Incremental-
construction searching' with the help of suitabe diagram.
8. What is the importance of scoring in docking?
9. Write note on following scoring functions
a) Force-Field Based scoring,
b) Empirical Scoring Function,
c) Knowledge-Based scoring function
10. Which scoring function is more suitable when we consider molecular
interaction in aqueous conditions and why?
Que1: What do you mean by drug-likeness and drug ability? Explain ‘rule of five’ for screening
of drug like molecules.
2. Ans: Drug -likeness:Drug-likenessisaqualitativeconceptusedin drugdesignforhow "drug-like"a
substance iswithrespecttofactors like bioavailability.Itisestimatedfromthe molecularstructure
before the substance isevensynthesizedandtested.
Drug-ability:Druggability isaterm usedin drugdiscovery todescribe abiological target(suchas
a protein) thatisknownto or ispredictedtobindwithhighaffinitytoa drug. Furthermore,bydefinition,
the bindingof the drug to a druggable targetmustalterthe functionof the target witha therapeutic
benefittothe patient.The conceptof druggabilityismostoftenrestrictedto small molecules (low
molecularweightorganicsubstances) butalsohasbeenextendedtoinclude biologicmedical
products such as therapeuticmonoclonal antibodies.
Despite technology advancement in the drug designing process, most drug discovery projects fail
because of the druggability problem. To avoid the failure of a drug discovery project, which very
expensive. It is very important to understand the difficulties associated with a potential target. Druggability
has become part of the target identification and validation process, more significantly in the case where
targets do not belong to traditional classes.
‘Rule of five’ for screening of drug like molecule:
Lipinski'srule of five,alsoknownasPfizer's rule of five orsimplythe rule of five (RO5),isarule of
thumbto evaluate drug-likenessordetermine if achemical compoundwith acertainpharmacological or
biological activityhaschemical propertiesandphysical propertiesthatwouldmake italikelyorally
active drug inhumans.
The rule was formulated by Christopher A. Lipinski in 1997, based on the observation
that most orally administered drugs are relatively small and moderately lipophilic
molecules.
The rule describes molecular properties important for a drug’s pharmacokinetics in the
human body, including their absorption, distribution, metabolism and excretion
(“ADME”).
The rule is important to keep in mind during drug discovery when a pharmacologically
active lead structure is optimized step-wise to increase the activity and selectivity of the
compounds as well as to ensure drug-like physicochemical properties are maintained as
described by Lipinski’s rule.
Candidate drugs that conform to the RO5 tend to have lower attrition rates during clinical
trials and hence have an increased chance of reaching the market.
Lipinski’s rule states that, in general, an orally active drug has no more than one violation of the
following criteria:
The molecular weight is greater than 500dalton.
The compound’s lipophilicity, expressed of the partition coefficient between water and 1-
octanol), is less than 5.
The number of groups in the molecule that can donate hydrogen atoms to hydrogen
bonds (usually the sum of hydroxyl and amine groups in a drug molecule) is less than 5.
3. The number of groups than can accept hydrogen atoms to from hydrogen bonds
(estimated by the sum of oxygen and nitrogen atoms) is less than 10.
Note that all numbers are multiples of five, which is the origin of the rule’s name. As with many
other rules of thumb, (such as Baldwin’s rules for ring closure), there are many exceptions to
Lipinski’s Rule.
The rules, based on the 90- percentile values of the drug’s property distributions, apply
only to absorption by passive diffusion of compounds through cell membranes;
-compounds that are actively transported through cell membrane by transporter proteins are
exceptions to the rule.
Physico-chemical criteria, such as those defined by the Lipinski rule of five, are typically used to
predict whether a compound is drug-like or not.
1. The bar chart shows the number of oral drugs that fall or pass the Lipinski rule, based on
a set of 771 drugs approved by the US Food and Drug Administration.
Hopkins and colleagues report a method for predicting the drug-likeness of compounds on a
scale of 0 (not drug-like) to 1 (drug- like). The chart shows the distribution of drug-likeness
calculated using this method for the same drugs depicted in a.
The analysis shows an overlap of Lipinski passes and failure for a range of drug-likenesses,
Notably, some very drug-like molecules fall the Lipinski rule, whereas some very un-drug-like
compounds pass it.
Que2: Explain Pharmacophore/ligand based virtual screening of libraries.
Ans: Ligand Based Virtual Screening: In LBVS process, the most effective biologically active
lead molecule is detected using structural or topological similarity or Pharmacophoric similarity
4. search. Taking into consideration several criteria-such as structure as well as shape of
individual fragments or electrostatic properties of the molecule carries out the similarity
comparisons.
The leads generated are ranked based on their similarity score, obtained using different
methods or algorithms.
Similarity based: A quantitative measure of similarity between two sets of molecular
descriptors.
1 Molecular similarity search based screening
1a. Small Molecule Alignment: In small molecular alignment the detection of similarity is
carried out by superimposing each of the test molecules of the database with the reference
molecule, and based on their extent of similarity they are ranked.
Generally in the super-position process the test molecule is taken as flexible, and the reference
molecule can be rigid or flexible. For example in Flexs algorithm, the reference molecule is
considered as rigid.
2b. Fragment based superimposition: Fragment based superimposition processes are also
used in several similarity search algorithms in determining the bioactivity of molecule.
-For example Fflash program uses graph based clique detection procedure using the
fragment based comparison of feature patterns.
2. Descriptor similarity Based Screening
5. In molecular alignment technique, a single molecule comparison takes a considerable amount
of time.
Hence the descriptor representation of the molecule is introduced and being used for searching;
which has been proved to be more efficient aid in searching the large chemical databases
2.1) 1-D and 2D- Descriptors: Bulk properties like Molecular weight, Molar refractivity, log P are
in general considered as one-dimensional (1D) descriptors of a molecule
-a) Binary Descriptor in binary descriptor representation, the presence of structural properties
for each position of lead molecule is narrated by means of a Boolean bit set to ‘one’ otherwise to
‘zero’.
-b) Real-value Descriptors: The real value descriptor vectors represent the Pharmacophoric
site of a lead compound by generating a graph.
2) 3D Descriptors: 3D similarity search is based on the concept that molecule with similar
conformational features shows similar biological activity i.e. Pharmacophore.
A candidate ligand can compare to the Pharmacophore model to determine whether it is
compatible with it and therefore likely to bind.
The estimation of similarity in descriptor-based analysis is also based on different
framework of descriptor (3D descriptors) and the different coefficient used in this search
procedure.
The 3D descriptors can be generated using different programs such as 3D –FEATURE
based on different hydrophobic groups, hydrogen bond acceptor and hydrogen bond
donor.
The ligand can also be transformed from 2D to 3D by means of programs like CORINA.
Numerous descriptors can also be generated by taking into consideration the functional
groups and primary shape properties.
Que3: What do you mean by Virtual screening? How it is crucial in CADD?
Ans: Virtual screening: Virtual screening refers to computational and a range of in-silico
techniques used to search large compounds databases to select a smaller number for biological
testing. Virtual screening can be used to –select compounds for screening from in house
database choose compounds to purchase from external suppliers decide which compounds to
synthesis next. The technique applied depends on the amount of information available about
the particular disease target.
Virtual screening is basically two types-
1. Structure Based Virtual Screening (SBVS): When virtual screening has used the
structural knowledge of receptor, it is known as structure based screening. It involves
automated and fast docking of a large number of chemical compounds against a protein-
binding or active site, directing a way to use the rapidly increasing number of protein 3D-
struture.
6. 2. Ligand Based Virtual Screening (LBVS): When structure of receptor is not known
virtual screening use alternative approach, which is based on the structure
representative of known ligands for a particular receptor (Pharmacophore), such
approach is known as ligand/ Pharmacophore based virtual screening.
Scope: Virtual screening (vs) is an important component of cheminformatics, CADD and
molecular modeling.
-An abundance of structural information, indicated by both the ever-increasing availability of 3-
dimensional (3D) protein structures and the readiness of free conformational databases of
commercially available compounds, such as ZINC, supplies a broad platform for VS.
-At the same time, new technology enables the implementation of more accurate and
sophisticated Pharmacophore models and the screening of millions of compounds within a
manageable period.
Crucial role in CADD:
The experimental efforts to carry out the biological screening of billions of compounds
are still considerably high, and therefore, computer-aided drug design approaches have
become attractive alternatives.
In recent years, virtual screening has reached a status of a dynamic and lucrative
technology in probing for novel drug-like compounds or so called hits in the
pharmaceutical industry.
Structure-based drug design (SBDD) and ligand-based drug design (LBDD) are the two
basic approaches of computer-aided drug design (CADD) used in modern drug
discovery and development program. Virtual screening (or in silico screening) has been
used in drug discovery program as a complementary tool to high throughput screening
(HTS) to identify bioactive compounds. It is a preliminary tool of CADD that has gained
considerable interest in the pharmaceutical research as a productive and cost-effective
technology in search for novel molecules of medicinal interest. In recenttimes,the use of
VStechniqueshasbeenshowntobe an excellentalternative tohighthroughputscreening,
especiallyintermsof cost-effectivenessandprobabilityof findingthe mostappropriate result
througha large virtual database.
Virtual Screeningfordrugdiscoveryisbecominganessentialtool toassistinfastand cost-
effectiveleaddiscoveryanddrugoptimisation.Thistechniquecanaidinthe discoveryof
bioactive molecules,since theyallow the selectionof compoundsinastructure database that
are mostlikelytoshowbiological activityagainstatarget of interest.VStoolsplayaprominent
role amongthe strategiesusedforthe identificationof new bioactivesubstances,since they
increase the speedof the drugdiscoveryprocessaslongas theyautomaticallyevaluate large
compoundlibrariesthroughcomputational simulations.
Que4: What is docking? Explain rigid and flexible docking with the help of suitable
example.
7. Ans: Docking: Docking is a method which predicts the preferred orientation of one molecule to
a second when bound to each other to form a stable complex. Knowledge of the preferred
orientation in turn may be used to predict the strength of association or binding affinity between
two molecules using, for example, scoring functions.
Docking has been a capable choice for the modeling of 3-dimentional structure of the
receptor-ligand complex and evaluating the stability of the complex that determines the
specific biological recognition.
In a simple definition, docking is a technique/method or process that is used to predict
how a protein (enzyme) interacts with small molecules (ligands). The ability of a protein
(enzyme) and nucleic acid to interact with small molecules to form a supramolecular
complex plays a major role in the dynamics of the protein, which may enhance or inhibit
its biological function.
The docking problem can be subdivided into two steps-
Exploring the conformational space of ligands that bind to target molecules
Scoring this set, i.e. ranking it in according to the estimated binding affinity.
Rigid Docking: In the rigid molecule docking problem we will relate to the molecules as rigid
body and components are not allowed to modify at any stage.
Example of Rigid Docking: Matching algorithm
Matching algorithm (MA): Several docking procedures have been classed here as matching
algorithms, as they common method of aligning structural features of the protein and ligand.
The constraints a pattern-matching algorithm can be based on shape and/or chemical
information. Frequently, complementary atom types such as hydrogen bond donors and
acceptors are paired between the protein and ligand.
Clique detection, a pattern-matching technique from graph theory, can be used to match
structural (physiochemical) features of ligand and receptor pockets.
- For example, a set of atoms with given chemical properties can be matched to a set of
complementary atom positions within the binding pocket, and the inter-atomic distances
between ligand atoms provide constraints on solution set.
- Generally these algorithms are applied to a rigid body or sets of rigid bodies
representing possible ligand conformations. Pattern-matching for ligand-docking ZDOCK
and FLOG.
Fast Shape Matching (SM)/Geometric Hashing:Shape matching algorithms are approaches
that take into account the geometric overlap between two molecules.
-Different algorithms are employed in order to make several alignments between and
receptor.
-This approach may identify the possible binding sites of a protein by a macromolecular surface
search.
8. Flexible Docking: In this we consider the molecule as flexible body and permit conformational
changes.
(a) Random searching
(b) Systematic searching
(c) Simulation based searching
Example of Flexible Docking: Genetic Algorithm
Genetic Algorithm: Genetic algorithm is based on Darwinian evolution-survival of the fittest
and decent with modification.
In a GA, there is a population of solutions that undergo unary (mutation) and higher
order (crossover) transformations. The newly generated solutions undergo selection,
biased towards the fit among them.
The algorithm maintains a selective pressure towards an optimal solution, with
randomized information exchange permitting exploration of the search space.
Example of application of genetic algorithm to docking procedures includes GOLD,
AutoDock, DARWIN,DOCK.
Que5: What is conformational search space? Explain it with respect to the docking.
Ans: Inthe case of protein docking, the searchspace consistsof all possible orientationsof the protein
withrespecttothe ligand. Flexible dockinginadditionconsidersall possible conformations of the
proteinpairedwithall possible conformationsof the ligand.
The search space in theory consists of all possible orientations and conformations of the protein
paired with the ligand. However, in practice with current computational resources, it is
impossible to exhaustively explore the search space—this would involve enumerating all
possible distortions of each molecule (molecules are dynamic and exist in an ensemble of
conformational states) and all possible rotational and translational orientations of the ligand
relative to the protein at a given level of granularity. Most docking programs in use account for
the whole conformational space of the ligand (flexible ligand), and several attempt to model a
flexible protein receptor. Each "snapshot" of the pair is referred to as a pose.
A variety of conformational search strategies have been applied to the ligand and to the
receptor. These include:
Systematic or stochastic torsion searches about rotatable bonds
Molecular dynamics simulations
Genetic algorithms to "evolve" new low energy conformations and where the score of each pose
acts as the fitness function used to select individuals for the next iteration.
Que6: What is the concept behind random searching? Explain Tabu search and Monte-
carlo search, how they differ with each other?
9. Ans: Random search: These searchalgorithmsoperate bymakingrandomchangestoeithera single
ligandor a populationof ligands.A newlyobtainedligandisevaluatedonthe basisof a predefined
probabilityfunction. Example,GA,Tabusearch and Monte Carlo
Tabu Search algorithm:Tabu search (ithas a memorystructure),combinesaminimizationprocedure
withrestrictionsonthe searchpath,such that the solutionisforcedintopreviouslyunexploredregions
on the searchspace.It proceedsstepwisefromaninitial solution, while maintainingalistof previous
solutions.
The listof previoussolutionsprovidesbotharankingof solutionsanda partial recordof
exploredregionsof the searchspace.
The Tabu search algorithmgeneratesasetof N new solutionsfromthe previous solution,and
one of N solutioniskept.A solutionisaddedtothe listif itis the bestsolutionsofar,or the
solutionof exploresanewregionsof the searchspace.
The Tabu search algorithmhasbeenusedinthe PRO_LEADS, dockingalgorithm, andin
conjunctionwithgeneticalgorithms.
Monte Carlo Algorithm: It algorithmsgeneratesainitial configurationof ligandinanactive site,
consistingof a randomconformation(translationandrotation).
-Score the initial configuration
- Generate anewconfigurationanscore it
-Use Metropoliscriterion(If ascore of new configurationisbetterthanthe previousone,itwill
immediatelyaccepted.If aconfigurationisnota minimum, aboltzman-basedprobabilityfunctionis
applied.Now,if the conformation passesthe probabilityfunctiontest,itwillaccepted,otherwise
rejected) todetermine whetherthe new configurationisretained.
-Repeatpreviousstepsuntil the desirednumberof configurationare obtained.
Que7: What do you mean by systematic searching? Explain ‘Incremental –construction
searching’ with the help of suitable diagram.
Ans: systematicsearch: These method/algorithmsare tryto explore all degree of freedomina
molecule,butultimatelyface the problemof combinatorialexplosion.Therefore ligandsare often
incrementallygrownintoactive sites.
A stepwise/incrementalsearchcanbe accomplishedindifferentways,fore.g.bydockingthem
covalentlyoralternativebydividingdockingligandsintorigid(core fragment) andflexibleparts(side
chain).Ex.FlexX,Dock.
Incremental construction (IC):Incremental constructionalgorithmsuse apiecewiseassemblyof the
ligandwithinadefinedbindingpocketinthe receptor.Rigidfragmentsare generatedfromthe ligandby
breakingitat rotatingbinds,tocreate a setof fragmentstobe usedby the dockingalgorithm.One or
10. more of these fragments, usuallythe largest,isselectedasthe starting/base fragmentsandisdockedto
the receptor.
A setof possible dockedorientationsforthisfragmentiskept,andotherfragmentsare addedinvarious
orientations.Afterthe fragmentsare dockedthe parts are fusedtogetherandthenscored.Thisprocess
isrepeateduntil the entire ligandisassembled.
Que8: What is importance of scoring in docking?
Dockingapplicationsusingincremental constructioninclude FlexX,Hammerhead,HOOK,and as a
componentof DOCK_4.0 and Surflex.De novoliganddesignalgorithmsusingincremental construction
include LUDIand GROWMON.
Ans: Scoring: The evaluationandrankingof predictedligandconformationisacrucial aspectof
structure-basedvirtualscreening.
-Evenwhen bindingconformationsare correctlypredicted,the calculationsultimatelydonotsucceedif
theydo notdifferentiate correctposesfromincorrectones.
-Sothe designingof reliable scoringfunctionandschemesisof fundamental importance.
Scoring functionsare approximate mathematical methodsthatusedtopredictthe strengthof the non-
covalentinteraction(alsorefferedtoasbindingaffinity) betweentwomolecules. The evaluationand
rankingof predictedligandconformationisaactual aspectof structure-basedvirtual screening.
Goals/Importance:
The firstrequirementforauseful scoringfunctionistobe able todistinguishthe experimentally
observedbindingmodes –associatingthemwiththe lowestbindingenergiesof the energy
landscape – fromall the other posesfoundbythe searchalgorithm(poseprediction).
The secondgoal isto classifyactive andinactive compounds(VS),andthe thirdisthe prediction
of the absolute bindingaffinity,rankingcompoundscorrectlyaccordingtotheirpotency
(binding affinityprediction).
The last one isthe mostchallengingtask,mainlyin denovo designandleadoptimization,since
small differencesinthe compoundcouldleadtodrasticchangesinbindingaffinity.
An ideal scoringfunctionwouldbe able toperformthe three tasks.However,givenseveral
limitationsof currentscoringfunctions,theyexhibitdifferentaccuraciesondistincttasksdue to
modelingassumptionsandsimplificationsmade duringtheirdevelopmentphase,being
intrinsicallyassociatedwiththe mainpurpose of the evaluated scoringfunction.
Dockingprotocolscan adoptdifferentscoringfunctionsforeachstep,e.g.,one canuse a fast
scoringfunctiontopredictbindingmodesandfurtherpredictaffinitiesemployingamore
sophisticatedscoringfunctionspecificforaffinityprediction.
Scoringfunctionsare typicallydividedintothree mainclasses:
(A) Force-FieldBasedscoring
13. Ans: Scoringfunctionsare usedtodiscriminate betweendifferentsolutionsevaluatingabroad range of
propertiesincluding,butnotlimitedtointermolecularinteraction,Desolvation,electrostatic,and
entropiceffects.Itcanbe classifiedasforce field-Based,empirical,andknowledge-Based.The scoring
functiontermare hydrophobiccomplementary,polarcomplementary,andentropy.The fourequations
relatedtostericscore,polarscore,polar repulsionscore,andentropyscore are usedtodefine the
scoringfunction.Forthe molecularinteractioninaqueous,the more suitable scoringfunctionamongst
the three isforce field-basedscoringfunction.A force fieldwithexpressesthe energyof the systemasa
sumof diverse non-bondedTerms(viz;vanderWaals(VDW) interactions,electrostaticinteractions,and
Bondstretching/binding/torsionforces),involvedinmolecularrecognition,are Usedforthe
developmentof force-fieldscoringfunctions.
Force-filedmethodsutilizeavarietyof Force-fieldparameters.Empirical scoringfunctionuses
several intermolecularinteractionterms whichare calibratedwithmaximumpossible
experimental data.The ideathatbindingenergiescanbe approximatedbyasumof individual
uncorrelatedtermsisusedindesigningof these functions.A typical semi-empirical force-field
scoringfunctionused inmoleculardockingthroughDOCKiscomparedof twoenergy
componentsof Lennard-JonesPotentialandanelectrostatictermwhose energyparametersare
takenfromthe Amberforce-field.
The common wayof introducingthe effectof desolvationtermsistotreatwatermolecules
explicitly.Howeverthese methodsare computationallyexpensive.The computational costis
reducedbytreatingwateras continuumdielectricmedium.
These modelsincludespoission- Boltzmansurface area(PB/SA ) andGeneralized-Bornsurface
area (GB/SA ) whichare oftenusedinpost scoringof the dockingprograms.
In some simplifiedscoringsolvationeffectinligandbindingfree energycalculationsis
performedusingaGB/SA approach.
The electrostaticinteractionsandthe electrostaticdesolvationcostsare calculatedwiththe GB
model while the hydrophobiccontributionsfornon-polaratomsare estimatedusingthe
solvent-accessiblesurface area(SA) of the atoms. Lennard-Jonespotentialisutilizedforthe
estimationof van-derwaalsenergies.