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Dominique Douguet
Criblages et Méthodologies
In silico
Ecole Thématique de Criblage 2-4 Octobre 2018 Carry-Le-Rouet
Institut de Pharmacologie Moléculaire et Cellulaire, UMR 7275 CNRS - Université Nice Sophia Antipolis, France
Genomic platform, ion channels, small G proteins, vesicular
transport, immunology
Pharmacology &
Neurosciences
ChemInfoScreen
Chimiothèque Nationale ChemBioScreen
ChemInfoScreen ADME-Tox Evaluation
Hit-to-Lead optimization programs to Explore and Cure living systems
• HTS assay optimization • Hit Identification
• Pharmacokinetics • Structure-Activity Relationships (SAR)
ChemInfoScreen: Cheminformatics Platform
To date: 8 sites + Coordinating site at UGCN
Nice
Strasbourg
Marseille
Paris
Orléans
Montpellier
UGCN (Philippe Jauffret) CBS (Gilles Labesse)
CRCM (Xavier Morelli)
IPMC (Dominique Douguet)
ICOA (Pascal Bonnet)
BFA (Pierre Tufféry) Institut Pasteur (Olivier Sperandio)
LIT (Didier Rognan) CMC (Alexandre Varnek)
~ 10-14 years/~1 Billion $
Pathology
Identified Target
Protein Sequence
3D Structure
Known Ligands Yes No
Identified Hits
Clinical trials
Approved drug
Lead Optimisation
Target Drug Discovery (TDD)
IR FRISBI
Medicinal Chemistry
.~ 10-14 years/~1 Billion $
Pathology
Identified Target
Protein Sequence
3D Structure
Known Ligands Yes No
Identified Hits
Clinical trials
Approved drug
Lead Optimisation
Target Drug Discovery (TDD)
Chimiothèque Nationale
ChemBioScreen
ChemInfoScreen
ADME-Tox
Medicinal Chemistry
IR FRISBI
.~ 10-14 years/~1 Billion $
Pathology
Identified Target
Protein Sequence
3D Structure
Known Ligands Yes No
Identified Hits
Clinical trials
Approved drug
Lead Optimisation
Target Drug Discovery (TDD)
Virtual Screenings
Chimiothèque Nationale
ChemBioScreen
Libraries Design
ChemInfoScreen
Raw data analysis
and SAR building
Synthesis
Bio-Profiling & ADME-Tox
Prediction
CN subset approved drugs commercial cpds
ligand-based structure-based
ADME-Tox prioritizing cpds prioritizing reactants scaffold hopping
Metabolism (site, CYP450s…) Off-targets
PAINS/reactivity alert analogs in catalogs properties prediction LogP, Sol.,Kd, Kon/Koff
Medicinal Chemistry
IR FRISBI
.~ 10-14 years/~1 Billion $
Pathology
Identified Target
Protein Sequence
3D Structure
Known Ligands Yes No
Identified Hits
Clinical trials
Approved drug
Lead Optimisation
Target Drug Discovery (TDD)
Virtual Screenings
Chimiothèque Nationale
ChemBioScreen
ChemInfoScreen
ligand-based structure-based
ADME-Tox
Docking
Ligand- and Structure-based Screenings
3D experimental structure/model of the target
Ligand-based Structure-based
3D • Pharmacophore
• Shape
2D/3D QSAR model (requires a large dataset)
N Ar
Od1 = 9-10 Å
d2 = 3-4 Å
d3 = 6-7 Å
bit set if the feature is present
Known ligands 2D • Graph/substructure • Fingerprint (eg: ECFP4)
Medicinal Chemistry
IR FRISBI
.~ 10-14 years/~1 Billion $
Pathology
Identified Target
Protein Sequence
3D Structure
Known Ligands Yes No
Identified Hits
Clinical trials
Approved drug
Lead Optimisation
Target Drug Discovery (TDD)
Virtual Screenings
Chimiothèque Nationale
ChemBioScreen
Libraries Design
ChemInfoScreen
CN subset approved drugs commercial cpds
ligand-based structure-based
ADME-Tox
Chemical space
1020-1060 ‘druglike’ molecules Chemical universe
Weininger D., Encyclopedia of Computational Chemistry, Vol 8,p1056; Bohacek RS. et al., Med. Res. Rev., 1996; Ertl P., J.Chem.Inf.Comput. Sci., 2003. ‘Druglike’: C, N, O, S, P, H, Cl, Br, F, I and MW ≤ 500 (Dobson C.M., Nature, 2004); Walters W.P., J. Med. Chem., 2018.
« The chemist as astronaut: Searching for biologically useful space in the chemical universe » D. Triggle, Biochem.Pharmacol., 2009.
Bar
naby
Rop
er
Chemical Space & Screening
Dune of Pilat
atoms in the Universe1
1060 1080 1020 1017 108
● CAS: ~100.106 (organics/inorganics) ● Dedicated to Pharmacology: Commercial: 106 (screening libraries) Naturals: 106 (theoretically) < 0.1.106 (isolated (10%)3) Toxins: 20.106 (theoretically) ~0.2.106 (UniProtKB (1%)4) Drugs: < 2000 FDA approved small-molecule drug structures (MW ≤ 2000)
isolated molecules ‘druglike’ molecules2 sand grains seconds
age of the Earth
2 ‘Druglike’: C, N, O, S, P, H, Cl, Br, F, I and MW ≤ 500 (Dobson C.M., Nature, 2004) 3 Harvey A., Drug Discovery Today, 2000 1 Source: C. Magnan, Collège de France, http://www.lacosmo.com/dixpuissance80.html 4 Zhang Y, Dongwuxue Yanjiu, 2015
- What is the usable size of a chemical library?
- Experimental High Throughput Screening (HTS) * A screening campaign may assay up to 500 000 compounds / week a low cost estimate ~ 0.40 $ / compound 1 (1 million compounds = 400 000 $) (includes cost of the chemical synthesis, high-throughput-screening disposables, capital costs and human resources)
several side issues: molecule re-supply, solubility, chemical stability, presence of PAINS (false positives)… as well as the management of waste products !
* It is commonly accepted that the suitable size of a library is ~250 000 to optimize the likelihood of finding a hit 2,3
Chemical Space Chemical space
1 Lipinski C. and Hopkins A., Nature, 2004, 432, 855-860. 3 Baell J, ACS Med Chem Lett, 2018. 2 Hibert M. and Haiech J., médecine/sciences, 2000, 16, 1332-9.
Pyridoxine (13 atoms)
Chemical Space Chemical space
- What is the usable size of a chemical library?
- Virtual Screening * Building chemical structures Example of the GDB-131 database: (13 atoms [C, N, O, S, Cl]) (<< mean drug size) - Combinatorial enumeration of structures - 3D Building, minimizing and validating structures
Results: 910 111 673 structures 39 882 (h) CPU time (= 1661 days of computation on 1 processor) ~0.16s /molecule (540 000 molecules / day / processor)
* Evaluating properties and/or interactions (e.g.: calculating the binding free energy ∆G of a ligand-protein complex)
- Using empiric method (docking method): ~20s to 3 min /molecule followed by visual inspection
- Using Molecular Dynamic (MD): hours to few days of calculation /molecule
1 Blum LC, Reymond JL.., J Am Chem Soc. 2009, 31(25), 8732-3.
Medicinal Chemistry
IR FRISBI
.~ 10-14 years/~1 Billion $
Pathology
Identified Target
Protein Sequence
3D Structure
Known Ligands Yes No
Identified Hits
Clinical trials
Approved drug
Lead Optimisation
Target Drug Discovery (TDD)
Virtual Screenings
Chimiothèque Nationale
ChemBioScreen
Libraries Design
ChemInfoScreen
Raw data analysis
and SAR building
CN subset approved drugs commercial cpds
ligand-based structure-based
ADME-Tox
PAINS/reactivity alert analogs in catalogs properties prediction LogP, Sol.,Kd, Kon/Koff
.~ 10-14 years/~1 Billion $
Pathology
Identified Target
Protein Sequence
3D Structure
Known Ligands Yes No
Identified Hits
Clinical trials
Approved drug
Lead Optimisation
Target Drug Discovery (TDD)
Hit-to-Lead
Lead Optimization
Drug
MW [1-200] LogP [0.5-4]
A hit ~ a molecule with µM range of activity
MW < 500 LogP < 5 nbHA<5, nbHD<10
.~ 10-14 years/~1 Billion $
Pathology
Identified Target
Protein Sequence
3D Structure
Known Ligands Yes No
Identified Hits
Clinical trials
Approved drug
Lead Optimisation
Target Drug Discovery (TDD)
Hit-to-Lead
Drug-like hits
Lead-like hits
High affinity hits
> 0.1 µM MW > 350 LogP > 3
> 0.1 µM MW < 350 LogP < 3 (polar)
<< 0.1 µM MW >> 350 LogP < 3
MW LogP
unfavored
Lead Optimization
Drug
MW [1-200] LogP [0.5-4]
A hit ~ a molecule with µM range of activity MW
LogP
Teague et al., Angew. Chem. Int. Ed., 1999
MW < 500 LogP < 5 nbHA<5, nbHD<10
.~ 10-14 years/~1 Billion $
Pathology
Identified Target
Protein Sequence
3D Structure
Known Ligands Yes No
Identified Hits
Clinical trials
Approved drug
Lead Optimisation
Target Drug Discovery (TDD)
Hit-to-Lead
Drug-like hits
Lead-like hits
High affinity hits
> 0.1 µM MW > 350 LogP > 3
> 0.1 µM MW < 350 LogP < 3 (polar)
<< 0.1 µM MW >> 350 LogP < 3
MW LogP
unfavored
Lead Optimization
Drug
MW [1-200] LogP [0.5-4]
A hit ~ a molecule with µM range of activity MW
LogP
LE > 0.35 ; LLE > 5 ; PFI < 7 LE = pX50*1.37 /#heavy atoms (kcal/mol/atom) LipE = LLE = pX50 - cLogP PFI = Chrom LogDpH7.4 + #Ar rings iPFI = Chrom LogP + #Ar rings
Leeson and Springthorpe, Nat Rev Drug Discov, 2007. Leeson and Young, ACS Med. Chem. Lett., 2015. Young and Leeson, J. med. Chem., 2018.
Teague et al., Angew. Chem. Int. Ed., 1999
MW < 500 LogP < 5 nbHA<5, nbHD<10
.~ 10-14 years/~1 Billion $
Pathology
Identified Target
Protein Sequence
3D Structure
Known Ligands Yes No
Identified Hits
Clinical trials
Approved drug
Lead Optimisation
Target Drug Discovery (TDD)
Hit-to-Lead
Drug-like hits
Lead-like hits
High affinity hits
> 0.1 µM MW > 350 LogP > 3
> 0.1 µM MW < 350 LogP < 3 (polar)
<< 0.1 µM MW >> 350 LogP < 3
MW LogP
unfavored
Lead Optimization
Drug
MW [1-200] LogP [0.5-4]
A hit ~ a molecule with µM range of activity MW
LogP
LE > 0.35 ; LLE > 5 ; PFI < 7 LE = pX50*1.37 /#heavy atoms (kcal/mol/atom) LipE = LLE = pX50 - cLogP PFI = Chrom LogDpH7.4 + #Ar rings iPFI = Chrom LogP + #Ar rings
Leeson and Springthorpe, Nat Rev Drug Discov, 2007. Leeson and Young, ACS Med. Chem. Lett., 2015. Young and Leeson, J. med. Chem., 2018.
Teague et al., Angew. Chem. Int. Ed., 1999
Identifying good – progressable - Hits
MW < 500 LogP < 5 nbHA<5, nbHD<10
Drug e 3D
Searches by: Names Substructures keywords Target name…
http://chemoinfo.ipmc.cnrs.fr
Pharmacokinetic data set
rings , fused rings and acyclics ( linkers and substituants)
Drug-like Fragments and Frameworks
(Bemis & Murcko definition)
X : anchoring point for substituents
Tota
l num
ber o
f dru
gs c
onta
inin
g th
e fr
amew
ork
10 … 496
… …
…
Framework type
1939 1939
1946 1942
1940 1949
1946
1939 1939
1951
1953
1945
1960
24 54
Most populated frameworks in approved drugs
Drug frameworks
47% structures are represented by only 24 “frameworks”
Most frameworks are unique (represented by only 1 drug structure)
Pihan et al., Bioinformatics, 2012; Douguet D., ACS Med Chem Lett, 2018. http://chemoinfo.ipmc.cnrs.fr
Tota
l num
ber
Decade Decade
Tota
l num
ber
After 1980s a larger number of new frameworks … but most populated frameworks are oldest ones
Most populated frameworks in approved drugs
Drug frameworks
Pihan et al., Bioinformatics, 2012; Douguet D., ACS Med Chem Lett, 2018. http://chemoinfo.ipmc.cnrs.fr
Drug Frameworks
37 drugs
38 drugs
41 drugs
47 drugs
48 drugs
49 drugs
69 drugs
91 drugs
227 drugs
Sulfapyridine (1939) (Unknown target; Estrogen receptor (Diethylstilbestrol))
Histamine (1939) (H1 receptor) Angiotensin-converting enzyme (Captopril))
Mephenytoin (1946) (Nav ion channel) HIV reverse transcriptase (Zidovudine))
Chloroquine (1949) (Lactate dehydrogenase; Beta adrenergic receptor (Propranolol))
Theophylline (1940) (Phosphodiesterase; Calcium-activated K+ (SK) channel (Riluzole))
Meperidine (1942) (Mu type opioid receptor; Neprylisin(Sacubitril))
Diphenydramine (1946) (H1 receptor; Prostaglandin H synthase (Amfenac/Nepafenac))
Desoxycorticosterone (1939) (Aldosterone synthase; Glucocorticoid receptor (Halobetasol))
Butabarbital (1939) (GABA receptor; 20S proteasome (Ixazomib))
e-Drug3D: release of July 2016 (1557 princeps / 1822 different structures) - 1189 different scaffolds (out of 1697) - 512 different frameworks Source: http://chemoinfo.ipmc.cnrs.fr ; Pihan et al., Bioinformatics, 2012.
e-Drug3D: release of July 2016 (1557 princeps / 1822 different structures) - 1189 different scaffolds (out of 1697) - 512 different frameworks Source: http://chemoinfo.ipmc.cnrs.fr ; Pihan et al., Bioinformatics, 2012.
Drug Frameworks
Cephalothin (1974) (Penicillin-Binding Protein)
Imipramine (1959) (Noradrelanine Transporter)
Folic acid (1946) (vitamine B9; DHFR (Methotrexate))
12 drugs
11 drugs
11 drugs
12 drugs
13 drugs
14 drugs
15 drugs
18 drugs
24 drugs
Sulfathiazole (1945) (Unknown target; Dihydropteroate synthase (Sulfamethizole); NKCC1, CFTR (Furosemide))
Prochlorperazine (1956) (D2 Dopamine receptor)
Thiamine (1953) (Vitamine B1; Alpha adrenergic receptor (Clonidine)
Vidarabine (1976) (Adenosine deaminase)
Diazepam (1963) (GABA receptor)
Promethazine (1951) (H1 receptor)
e-Drug3D: release of July 2016 (1557 princeps / 1822 different structures) - 1189 different scaffolds (out of 1697) - 512 different frameworks Source: http://chemoinfo.ipmc.cnrs.fr ; Pihan et al., Bioinformatics, 2012.
Drug Frameworks
Clomiphene (1967) (Estrogen receptor)
11 drugs
Phenoxybenzamine (1953) (Alpha adrenergic receptor; Platelet glycoprotein (Tirofiban)) 10 drugs
Triamcinolone acetonide (1960) (Glucocorticoid receptor) 10 drugs
10 drugs
Tropicamide (1960) (Muscarinic acetylcholine receptor; Noradrenaline transporter (Benzphetamine))
10 drugs
Benzquinamide (1974) (P-glycoprotein receptor; Cannabinoid receptor (Dronabinol))
∑ (represented drugs) = 828/1822 = 45.4% of drug structures are represented by 23 frameworks
The simplest frameworks appeared first and are the most populated
e-Drug3D: release of July 2016 (1557 princeps / 1822 different structures) - 1189 different scaffolds (out of 1697) - 512 different frameworks Source: http://chemoinfo.ipmc.cnrs.fr ; Pihan et al., Bioinformatics, 2012.
Drug Frameworks
Cycrimine () (Muscarinic acetylcholine receptor M1)
2 drugs
Clidinium () (Muscarinic acetylcholine receptor)
2 drugs
Deslanoside () (Sodium/potassium ATPase)
1 drug
2 drugs
2 drugs Clidinium () (Muscarinic acetylcholine receptor)
2 drugs
Meclocycline () & Methacycline () (Ribosome)
Protokylol () (Beta 1/Beta 2 adrenergic receptor)
Methixene () (Muscarinic acetylcholine receptor)
2 drugs
Discontinued
Pentolinium () (antihypertensive)
1 drug
Pyrvinium () (anthelmintic)
Quinestrol () (Estrogen receptor)
Prazepam () (GABA receptor)
Trilostane () (Estrogen receptor)
Dezocine () (Kappa/Mu opioid receptor)
Hetacillin () (Penicillin-Binding Proetins 1A/1B)
Amdinocillin () (Penicillin-Binding Proetins 2B)
Candicidin () large polyene structure (membrane)
Ceruletide () large structure (Cholecystokinin type A)
Hexafluorenium () (Cholinesterase)
Viomycin () large ring (70S ribosome)
Beta carotene () (Beta carotene monooxygenase)
Saralasin () (Angiotensin II receptor)
Gentian violet () (NADPH oxidase)
e-Drug3D: release of July 2016 (1557 princeps / 1822 different structures) - 1189 different scaffolds (out of 1697) - 512 different frameworks Source: http://chemoinfo.ipmc.cnrs.fr ; Pihan et al., Bioinformatics, 2012.
Drug Frameworks
Discontinued Frame 13 Dicumarol (1944) (Xanthine oxidase)
Frame 14 Metocurine/Tubocarine (1945) (5-HT3 receptor)
Frame 41 Très proche de frame 1 (sulfapyridine) Hydroxystilbamidine (1953) antiparasitic (Unknown target)
Frame 47 Ambenonium (1956) (Cholinesterase)
Frame 53 Rescinnamine (1956) (Angiotensin-converting enzyme)
Frame 56 Diphenidol (1967)/Oxyphencyclimine (Muscarinic acetylcholine receptor)
Frame 64 Biperiden (1959) (Muscarinic acetylcholine receptor)
Frame 66 Benzthiazide (1960) (Antihypertensive)
Frame74 Chlorprothixene (1967) (D2 Dopamine receptor)
Frame 79 Cyclothiazide (1982) (Glutamate receptor 2)
Frame 90 Testolactone (1969) (CYP450 19-aromatase)
Frame 98 Pentagastrin (1974) (Cholecystokinin type B receptor)
Frame 100 Mazindol (1973) (Noradrenaline & Dopamine transporter)
Frame 119 Guanadrel (1982) (Antihypertensive)
Frame 131 Antazoline (1990) (Cav channel)
Frame 136 Bepridil (1990) (Ca channel)
Frame 161 Doxacurium (1991) (Muscarinic acetylcholine receptor M1)
Frame 173 Trovafloxacin/Alatrofloxacin (1997) (DNA gyrase)
Frame 174 Cisapride (1993) (5-HT4 receptor)
Frame 175 Levocabastine (1993) (Histamine H1 receptor)
Frame 213 Troglitazone (1997) (PPAR gamma)
Frame 239 Pemirolast (1999) (antiinflammatory)
Frame 245 Telithromycin (2004) large ring (50S ribosome)
e-Drug3D: release of July 2016 (1557 princeps / 1822 different structures) - 1189 different scaffolds (out of 1697) - 512 different frameworks Source: http://chemoinfo.ipmc.cnrs.fr ; Pihan et al., Bioinformatics, 2012.
Drug Frameworks
Discontinued
Frame 297 Cyclacillin / Methicillin (1979) (Penicillin-Binding Proetins 1A/1B)
Frame 303 Troleandomycin (1969) large ring (ribosome)
Frame 304 Novobiocin (1964) (DNA gyrase)
Frame 306 Cephapirin (1974) (Penicillin-Binding Proetins 1A/1B)
Frame 309 Ticarcillin (1976) (Penicillin-Binding Proetins 1A/1B)
Frame 314 Cefoperazone/Cefpiramide (1982) (Penicillin-Binding Proetins 1A/1B)
Frame 315 Azlocillin/Mezlocillin (1981) (Penicillin-Binding Proetins 1A/1B)
Frame 325 Cefmetazole (1989) (Penicillin-Binding Proetins 1A/1B)
Frame 327 Dirithromycin (1995) large ring (ribosome)
Frame 343 Guanethidine (1960) (Nitric oxide synthase)
Frame 346 Bitolterol (1984) (Beta 2 adrenergic receptor)
Frame 377 Hydrocortisone cypionate (1955) (Glucocorticoid receptor)
Frame 380 Nandrolone phenpropionate (1959) (Androgen receptor)
Frame 381 Sulfaphenazole (1974) (CYP450)
Frame 386 Protirelin (1976) (Hormone analog)
Frame 392 Nalmefene (1995) (Opioid receptor)
Frame 396 Plicamycin (1970) (Unknown target)
Frame 397 Carbenicillin indanyl (1972) (Penicillin-Binding Proetins)
Frame 417 Telaprevir (2011) (NS3/4A protease)
rings , fused rings and acyclics ( linkers and substituants)
(Bemis & Murcko definition)
X : anchoring point for substituents
Drug-like Fragments and Frameworks
Privileged Structures/Fragments
Mean number of : - legos in drug structures - legos in frameworks (rings + linkers) - substituants in drug structures
Pihan et al., Bioinformatics, 2012; Douguet D., ACS Med Chem Lett, 2018. http://chemoinfo.ipmc.cnrs.fr
Statistics on approved drugs*: Evolution of drug properties
Year
Mol
ecul
ar W
eigh
t
Year
Pola
r Sur
face
Are
a
Physico-Chemical Properties
*e-Drug3D: release of March 2015 (1746 different structures)
Statistics on approved drugs*: Evolution of drug properties
Physico-Chemical Properties
*e-Drug3D: release of March 2015 (1746 different structures) ** Fsp3 = Number of C(sp3) / Number of C
Year
LogP
Year
Fsp3
*
Privileged Structures/Fragments
Drug structures have gained weight over the years and are more complex (more legos)
An increase in the complexity of new frameworks (highly branched structures)
On average, the number of legos in a drug = 5.3 - 3.6 legos in the framework (rings + linkers) - 1.7 legos in ’decoration’ (substituants)
Example: Venetoclax (2016) (protein-protein inhibitor)
Pihan et al., Bioinformatics, 2012; Douguet D., ACS Med Chem Lett, 2018. http://chemoinfo.ipmc.cnrs.fr
.~ 10-14 years/~1 Billion $
Pathology
Identified Target
Protein Sequence
3D Structure
Known Ligands Yes No
Identified Hits
Clinical trials
Approved drug
Lead Optimisation
Target Drug Discovery (TDD)
Hit-to-Lead
MW < 500 LogP < 5 nbHA<5 nbHD<10
Drug-like hits
Lead-like hits
High affinity hits
> 0.1 µM MW > 350 LogP > 3
> 0.1 µM MW < 350 LogP < 3 (polar)
<< 0.1 µM MW >> 350 LogP < 3
MW LogP
unfavored
Lead Optimization
Drug
MW [1-200] LogP [0.5-4]
A hit ~ a molecule with µM range of activity MW
LogP
LE > 0.35 ; LLE > 5 ; PFI < 7 LE = pX50*1.37 /#heavy atoms (kcal/mol/atom) LipE = LLE = pX50 - cLogP PFI = Chrom LogDpH7.4 + #Ar rings iPFI = Chrom LogP + #Ar rings
Leeson and Springthorpe, Nat Rev Drug Discov, 2007. Leeson and Young, ACS Med. Chem. Lett., 2015. Young and Leeson, J. med. Chem., 2018.
Teague et al., Angew. Chem. Int. Ed., 1999
Identifying good – progressable - Hits
Lead-like compounds: MW 150-350 LogP 3 Rings 1-4 Hbond donor (nbHD) <5 Hbond acceptor (nbHA) <8 Exclude PAINS (Pan-Assay Interference compounds) -> false positive hits = apparent biological activity molecules interfere with the assays (aggregation, micelle, autofluorescence… ) interfere with the function of the protein (chemical reactivity (aldehydes, epoxides, acid halide…), metal chelation, redox activity…) e.g.: phenotypic assay & amphiphilic molecules (!! non specific activity through membrane binding) PAINS classes: rhodanines, quinones, cathechols… are well known frequent hitters Reproducible activity with Re-synthesized or Repurified molecule Additional biological assays (SPR, structural biology…) Demonstration of Structure-Activity Relationships (SAR) and Hit-to-Lead optimization
Good pract ices for HIT Ident ificat ion
O
O
S
N SO
OO
O O
OOcurcumin