Molecular Modeling in Drug Design
Martin Smieško
Molecular Modeling : Department of Pharmaceutical Sciences : University of Basel : Switzerland
Molecular Modeling in Drug Design
Part 5: Virtual Screening + Publication Search
M. Smieško – Departement Pharmazeutische Wissenschaften, Universität Basel, 2017
Molecular Modeling in Drug Design
Virtual Screening (VS)
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computational technique used in drug discovery to search libraries of small molecules (publicly
available, commercial, in-house, proprietary) in order to identify structures, which are most likely
to bind to a selected drug target
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in silico analog of biological high-throughput screening (HTS)
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score, rank, and/or filter a set of structures using one or more computational procedures
Motivation
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cheaper and time effective alternative to the HTS
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decrease the count of molecules than need to be screened experimentally
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new scaffold identification (patent protection, solubility, selectivity...) → lesson next week
M. Smieško – Departement Pharmazeutische Wissenschaften, Universität Basel, 2017
Molecular Modeling in Drug Design
High-throughput (experimental) or Virtual Screening
ligand(s) or
protein known?
HTS feasible ?
yes
no
time &
costs OK ?
yes
no
VS
no
protein
available ?
VS
no
diversity
needed ?
yes
yes
structurebased VS
VS
no
ligandbased VS
yes
HTS
VS
docking
& scoring
HTS and VS can be performed in a
complementary manner
M. Smieško – Departement Pharmazeutische Wissenschaften, Universität Basel, 2017
pharmacophore
& similarity
Molecular Modeling in Drug Design
Virtual Screening
increasing computational costs
decreasing number of compounds
public/commercial/company libraries
simple fast pre-filtering
(e.g. Lipinski, Veber)
in depth filters / similarity evaluation
molecular docking / scoring
(several stages/precisions)
consensus search
best hits (bought/synthesized/retrieved)
experimentally tested
success if a few real hits (Kd < 10 M) found
(enrichment = real hits/computed hits)
M. Smieško – Departement Pharmazeutische Wissenschaften, Universität Basel, 2017
Molecular Modeling in Drug Design
Virtual Screening Filters
AstraZeneca style :)
Cumming J.G. et al.: Chemical predictive modelling to improve compound quality.
Nat Rev Drug Discov. 2013, 12(12), 948-962
http://www.nature.com/nrd/journal/v12/n12/pdf/nrd4128.pdf
Initial property filter:
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CORE compounds: hit no chemical filters and fulfill the following property filters: molecular
weight between 100 and 550, cLogP between 2 and 6, polar surface area (PSA) between 1 and 160
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BACKUP compounds: fail on one property filter
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UGLY compounds: fail on 2 or more property filters or hit at least one chemical filter
The AZ screening collection was split into CORE, BACKUP and UGLY sets, based on these filters. Only
CORE and BACKUP compounds are solubilised for HTS, and usually only CORE compounds are
purchased from external vendors.
And the chemical filters are…
M. Smieško – Departement Pharmazeutische Wissenschaften, Universität Basel, 2017
Molecular Modeling in Drug Design
AZ Chemical Filters
Class 1: Bland Structures
(remove isotopes, unwanted elements, unbranched structures, structures where heteroatoms are
provided by only one functional group)
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disconnected structure: salts, hydrates, racemates >= 1
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alkyl/aryl amine (bin if >=1 and no other heteroatom)
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isotopes >= 1
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OH/SH (bin if >=1 and no other heteroatom)
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compounds containing atoms other than: H, C, N, O, S,
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(thio)ethers (bin if >=1 and no other heteroatom)
F, Cl, Br, I >= 1
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quaternary (bin if >=1 and no other heteroatom)
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<= 3 C atoms, must have at least 4 carbon atoms
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N-oxide (bin if >=1 and no other heteroatom)
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less than 12 heavy atoms
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halophenols
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no polar atoms (N, O, S)
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haloketones
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straight/unbranched structures
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mono and dinitro phenols
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positive charged atoms >= 1
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no aromatic carbon polyamines
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compounds with >=3 acidic groups
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only hetero is 1 acid or derivatives
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C-nitro (bin if >=1 and no other heteroatom)
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p-diaminobiphenyl
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C-C≡N (bin if >=1 and no other heteroatom)
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m- or p- diaminobenzene with no other heteroatoms
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sulphone SO2 (bin if >=2 and no other heteroatom)
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unusual valence (C, N, O, P, S, Hal)
http://www.nature.com/nrd/journal/v12/n12/pdf/nrd4128.pdf
M. Smieško – Departement Pharmazeutische Wissenschaften, Universität Basel, 2017
Molecular Modeling in Drug Design
AZ Chemical Filters
Class 2: Reactive structures
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isourea
Michael acceptors: C=C-C=O, C=C-CN, C=C-SO2, C=C-
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benzoquinone
NO2
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aliphatic and aromatic C-aldehyde RC(=O)H
acetylene Michael acceptors: C≡C-C=O, C≡C-CN, C ≡C-
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peroxides
SO2, C≡C-NO2
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Schiffs base/imine RN=CRR (excluding benzodiazepine,
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vinyl and Hal, C=C-Hal
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reactive beta keto quaternary O=CCN+
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reactive
cyclic imines, ArN=C(Ar) NO)
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imine halide
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N-halogen & S-halogen & P-halogen & O-halogen
(X=Hal/CN/Ph)
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diazetidinone
sulphonamide imine but not sulphonamide amidine nor
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azetidin-2-one
in ring
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epoxides & aziridines & thiiranes & oxazirane
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anhydride
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thiocyanate
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alpha halo (hal=1,2,3) ketone
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isocyanates & isothiocyanates
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halo methylene ether
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isocyanide = isonitrile -N≡C
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CH2Hal: CBr3, CBr2, CBr, CCl2, CCl; hal-C-C-R is OK
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azoanhydrides
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acid halide & thio acid halide
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1,3-benzodioxan-2,4-diones
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halo (incl. F) triazine or 2-, 4-pyrimidine but do not bin
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furazanes 4,7-disubstituted
2-amino, 4-Cl pyrimidine nor 2-amino, 4-Cl triazine
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nitrite
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esters
and
thioesters
RCOOCX2
http://www.nature.com/nrd/journal/v12/n12/pdf/nrd4128.pdf
M. Smieško – Departement Pharmazeutische Wissenschaften, Universität Basel, 2017
Molecular Modeling in Drug Design
AZ Chemical Filters
Class 3: Frequent hitters
Cass 5: Unlikely drug candidates
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more than 2 nitro groups
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di-OH benzene
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Large ring >= C9
nitro phenols
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C9 chain not in any rings
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crown ethers
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multi alkene chain C=CC=CC=C and N=CC=CC=C
Class 4: Dye-like structures
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diyne -C≡C-C≡C-
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>= 2 nitro on same aromatic ring including naphthalene
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annelated rings phenanthrene, anthracene, phenalene
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diphenyl ethylene cyclohexadiene
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all compounds with 4 fused aromatic rings and some
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aliphatic quaternary N and C9 chain
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SO3H/R plus benztriazole
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SO3H/R plus aminotriazine
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SO3H/R plus aminobenzene
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beta (mesyl or halo) amine or O/S mustard
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SO3H/R plus naphthalene
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two S-atoms (not sulfones SO2 ) in 5-rings or 6-rings
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quaternary aromatic N+(amine or aminoethylene)
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cyclobut-3-ene-1,2-dione
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quaternary pyridine
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triphenylmethyl
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neutral p-amino pyridine bin if >=2
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acridine
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& Unsuitable fragments
with 3 rings
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beta naphthylamine but not if alpha keto, nor if
substituted with aromatic carbon
http://www.nature.com/nrd/journal/v12/n12/pdf/nrd4128.pdf
M. Smieško – Departement Pharmazeutische Wissenschaften, Universität Basel, 2017
Molecular Modeling in Drug Design
AZ Chemical Filters
Class 6: Difficult series / Natural compounds
Cass 8: General UGLY Oxygen
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steroid
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too many OH's - bin if >= 5
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penicillin or cephalosporin
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p-, p'-dihydroxy biphenyl
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prostaglandin
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p-, p'-dihydroxy stilbene
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exocyclic double bond
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acids
(COOH/COSH/CSOH/CSSH)
(esters, lactones), bin if COOR >= 3
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di tBu ester
Class 7: General UGLY Halogenated Structures
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poly (CCO)
halogen counts: bin if (Cl + Br + I + X >= 4) OR (CF3 >=
●
formic acid esters
4) OR (Br + I >=2) where X can be F or CF3
●
triacyloximes
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di or trivalent halogens
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sugar
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N-halogen & S-halogen & P-halogen & O-halogen
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2,3,4, & 2,4,5 trihydroxyphenyl
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sulfonyl halide
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triflates SO3CX3
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perhalo ketones CH 2 C(=O)CX3
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perfluoroacetates
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C(Br)NO2
●
http://www.nature.com/nrd/journal/v12/n12/pdf/nrd4128.pdf
M. Smieško – Departement Pharmazeutische Wissenschaften, Universität Basel, 2017
and
derivatives
Molecular Modeling in Drug Design
AZ Chemical Filters
Class 9: General UGLY Nitrogen
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hydrazine RRN-NRR not in ring, nor diketo
hydrazone RRC=N-NRR and not in ring (the C can be
aromatic)
guanidine >=3
azine C=N-N=C and azide NNN and not in ring (see
azodiaz)
quaternary N >= 2
N oxide >= 2
phenanthroline [or di(2-imidazolyl)methane] chelator
CNOC in chain not in ring (keep hydroxamic acid)
azo N=N or diazonium N≡N
carbodiimide
N-nitroso
aromatic nitroso
geminal cyanides C(CN)2
cyanohydrins and (thio)acylcyanides
cloramidine
nitrite
nitramines
oxime
Cass 10: General UGLY Sulphur
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sulphur >= 5; disulfide >= 1; sulfate >= 1
R-SO3 -polar atom and R-SO2 -S-R, not sulfonic acids
nor esters
sulfonic acid; disulfone; thioketone
sulfonic esters except aryl/alkyl-SO 3 -aryl
cyclic –NSN- or –SNS- not -NS(=O)(=O)SN not sulphonamides; SO not S=O nor in ring
1,2-thiazol-3-one
dithiocarbamate NC(=S)S
CNS or CSO eg thiourea, isothiourea, thiocarbamic acid
or thiocarbonate
S+ but not S(O-)3
sulfonyl cyanides
isocyanates & isothiocyanates, thiocyanate
alkyl-SH, carbaryl-SH
dithioic and thioic acids O- and S-esters
sulphonamide imine (Michael) but not sulphonamide
amidine nor in ring
1,3-oxathiolane (not oxathiolane-3,3-dioxides)
dithioic and thioic acids
http://www.nature.com/nrd/journal/v12/n12/pdf/nrd4128.pdf
M. Smieško – Departement Pharmazeutische Wissenschaften, Universität Basel, 2017
Molecular Modeling in Drug Design
Virtual Screening Approaches
Ligand-based
- only 1 active molecule known: perform similarity search (structure, descriptors, fingerprints)
- a few active molecules known: identify 3D pharmacophore → perform a 3D database search
- multiple active and inactive structures known: train a machine learning technique (QSAR, CoMFA)
- typically faster
Structure-based
- many good algorithms for pose generation available (molecular docking)
- scoring functions are limiting factor for the reliability of poses and predictions (approximations,
water treatment, partial charges, entropic terms; general lack of reference systems)
- extremely complex → high computational costs
Are we about to see automated tools based on artificial intelligence?
We know what we want, but we do not yet have completely reliable methods. We can:
- define desired physico-chemical properties (pharmacokinetics, toxicology)
- find the active principle using 2D or 3D methods
- define feasible/accessible fragments and reactions to be used for chemical synthesis
- fully automatize processes → revolution in robotics → automatic synthesis and in vitro testing
http://www.nature.com/nrd/journal/v12/n12/pdf/nrd4128.pdf
M. Smieško – Departement Pharmazeutische Wissenschaften, Universität Basel, 2017
Molecular Modeling in Drug Design
How to find a suitable case study
&
How to design an electronic report
M. Smieško – Departement Pharmazeutische Wissenschaften, Universität Basel, 2017
Molecular Modeling in Drug Design
Literature search – where and how?
Search by topic (keywords) = Molecular Modeling in Drug Design :)
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try advanced queries – combine more specialized fields (keyword in the title, abstract, year)...
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ScienceDirect (http://www.sciencedirect.com/)
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PubMed (https://www.ncbi.nlm.nih.gov/pubmed/)
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General purpose search engines like Google, Yahoo, Bing, Wikipedia...
Search by journal/editing house (e.g. latest/most cited papers)
American Chemical Society Journals (pubs.acs.org):
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Journal of Chemical Information and Modeling (http://pubs.acs.org/journal/jcisd8)
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Journal of Chemical Theory and Computation (http://pubs.acs.org/journal/jctcce)
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Journal of Medicinal Chemistry (http://pubs.acs.org/journal/jmcmar)
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Journal of the American Chemical Society (http://pubs.acs.org/journal/jacsat)
Nature (http://www.nature.com/siteindex/index.html)
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Nature Reviews Drug Discovery (http://www.nature.com/nrd/)
Wiley (http://onlinelibrary.wiley.com/subject/code/000131) e.g. Angewandte Chemie int. Ed.
Tailor and Francis (http://www.tandfonline.com/), e.g. Expert Opinion on Drug Discovery
SpringerOpen (https://www.springeropen.com/journals), e.g. Journal of Cheminformatics
Royal Society of Chemistry (http://www.rsc.org/)
Elsevier (https://www.elsevier.com/books-and-journals)
...
M. Smieško – Departement Pharmazeutische Wissenschaften, Universität Basel, 2017
Molecular Modeling in Drug Design
No exam, but electronic report (PDF) of a case study
Papers selected should contain some point of novelty and be Pharma relevant
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published preferably in the last 5 years (= 2012 – 2017), if justified then at max. 7 years
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in a good (CADD) journal → good impact factor is important, but not critical
What to search for
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completely new topic, which has not yet been addressed in EiMM, CMoAE or MMiDD
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topic already reported (e.g. MD, docking), but specially tailored, extended, combined...
NO DUPLICATES, please!
What to do
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carefully read and understand
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write a short report 2-3 pages (12 font, 1.5 line spacing) with own words (do not copy the abstract!)
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may include some pictures/graphs/tables/outlines/schemes (own contribution appreciated)
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comment/mention motivation, main goal, method used, result, conclusion
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+ answers to the following questions:
- Why did you choose this particular publication?
- What did you like?
- What did you don’t like?
Idea behind:
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expose you to the literature, terminology, multitude of computational (supported) methods
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in depth reading of a topic that might be of interest for you
M. Smieško – Departement Pharmazeutische Wissenschaften, Universität Basel, 2017