CzeekD: Fragment-based de novo Drug Design System for Drug Discovery
Edmund Taylo Kyoto Constella Technologies Co., Ltd.
Kyoto, Japan
Corporate Information
Registered Name: Kyoto Constella Technologies Co., Ltd Establishment: March 31, 2008 LocaBon: Kyoto, Japan Business: Contract computaBonal services,
chemoinformaBcs related soIware development
Rapid screening for candidate compound
Compound design and opBmizaBon
Drug related adverse reacBon informaBon retrieval system
Basic Research
Non-‐clinical experiments
Clinical experiments (clinical trial)
Approval and
markeBng applicaBon
Grant of approval
Post-‐markeBng surveillance
From screening to de novo design
Efficient creation of new active compound structures through the combination of ligand-target interaction machine-learning and optimization algorithms
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Limited number of compounds (approx. 6 million compounds)
SelecBon of acBve compounds from the compound library
In silico screening
• Commercially available compound libraries
• In-‐house libraries�
InteracBon machine-‐learning
OpBmizaBon algorithm
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Over 1060 compounds
Highly diverse selecBon of acBve novel compounds
Random genera8on of compounds�
Efficiently search large
chemical space
SelecBon of high quality lead compounds
Enormous amount of computaBonal Bme
Over 1060 compounds
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Highly diverse selecBon of acBve novel compounds
Random genera8on of compounds�
De novo compound design (CzeekD)
Fragment Binding • RECAP • BRICS
Structure substitution,
addition, and deletion • Bioisosteres • MMP
Without protein structure • CGBVS • QSAR
With protein structure
• docking • binding free energy
Optimization method • Full search • Tabu search • Evolution algorithm (GA, etc.)
• Swarm intelligence(Particle Swarm Optimization; PSO)
Crea%on of compounds
Evalua%on Search of chemical space
1 = amide 2 = ester 3 = amine 4 = urea
5 = ether 6 = olefin 7 = quaternary nitrogen
9 = lactam N carbon
Compounds are fragmented based on RECAP rules
Fragmentation method
8 = aroma%c N carbon
10 = aroma%c carbon – aroma%c carbon 11 = sulphonamide
Example:
4 types of units can be joined together to create a target synthesis frame
Fragment units
○ ○
x Units with dangling bonds are not allowed
Virtual design of compounds (CzeekD)
Appropriate fragments are aPached based on the RECAP rules
× Fragment combina%ons not conforming to the RECAP rules are discarded.
CGBVS (Chemical Genomics-based VS)
0.10 -0.2 -0.4
NN
N
SOO
NN
N
O
N
NN
N
SO O
high score comp.
0.98 0.97
NN
N
O O
ON
N
N
O
O
SN O
O
Ligand-‐target predicBon
high score comp.
Compound optimization
NN
N
O
O
O
ON
SNN
N
OS
NO
O
NN
N
OS
N
N
O
ON
N
N
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ONN
N
O
O
O
O
0.54 0.65 0.95 1.12 1.25
structural features of compounds with high scores are conserved while creaBng successive generaBons of compounds
NN
N
O
O
SN O
ON
N
N
OS
N O
ON
N
N
OS
N O
ON
N
N
OS
NO
O
N O
NN
N
OS
N O
OF
1.51 1.52 1.52 1.53 1.53
repeat of generaBon cycle (5000x)
Enriched collecBon of compounds possessing chemical structures that suggest pharmacological acBvity
Evolution of molecules within the machine
Over 1015 possible combinaBons
105 combinaBons
105 combinaBons 105 combinaBons random generaBon of 128 compounds
Candidate compounds: β2AR: 29,815 V1bR: 38,088
A case study in de novo design(from library design to chemical synthesis)
CGBVS
PSO※
OpBmizaBon
Filter by constraints
Synthesis frame Core structure
Commercially available building blocks
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# of virtual compounds: β2AR : 9.6 × 108 V1bR : 2.3 × 109
Target Evaluated Results
IC50≦30μM IC50≦100μM
β2AR 104 40 65
V1bR 105 24 77
ImplementaBon of compound design, chemical synthesis and validaBon of lead discovery, shown here for GPCRs
Combinatorial chemistry
Compound hits with novel structures
Structure-activity relationship (SAR) data
β2AR antagonist SAR table V1bR antagonist SAR table
Substance Patent ApplicaBon (V1b antagonist) ApplicaBon No.: 2012-‐177572 (August 9, 2012) Applicants: ① Kyoto University
② Kyoto Constella Technologies Co., Ltd. ③ KNC Laboratories Co., Ltd. Ø Useful data for subsequent chemical synthesis
Ø Results of validation yielded hit compounds now under patent application
AcBvity (IC50) Side chain structure with effects to acBvity posi%ve nega%ve
Evaluation of specificity towards V1bR
3 compounds are evaluated for specificity against the GPCR family of proteins
Assay performed
V1bR
GPCR名
活性評価実験(+/-:50%阻害活性@30μM) A-Ac-11 C-Ac-11
A9-Ac-11
① ADRB2 - - - ② CHRM1 + - - ③ PTGER1 - - - ④ NPY2R - - - ⑤ V1AR + + - ⑥ V1BR + + + ⑦ V2R - - - ⑧ EDNRB - - - ⑨ CCR1 - - - ⑩ SSTR1 - - - ⑪ LHCGR - - - ⑫ P2Y1 - - - ⑬ VIPR1 - - - ⑭ GRM1 + + +
N N
O
N
ONO2
N N
O
N
OO
O2N
+/- 50% Inhibitory Activity at 30 uM
Settings Window
De novo compound design system: CzeekD
SelecBon of target proteins
Sefng the synthesis frame
SelecBon of target family
De novo design system: CzeekD
Results Analysis Window
Structure display
Substructure search
Summary
l Features v Fragment-‐based, syntheBcally accessible (RECAP) v Unique and rapid scoring funcBon (CGBVS) v Efficient search of chemical space (PSO) v Screening against mulBple target proteins v Capable of scaffold hopping v User-‐friendly interface v Models for GPCRs, Kinases, Ion channels , Transporters, Nuclear receptors
and Proteases
l Proven performance v GPCRs v Kinases v Ion-‐channels
l Service v ASP → SoIware demos (2 weeks) v Contract service
Arigatou gozaimasu! Thank you!