Evotec Investor Day Presentation Deck
evotec
1. Bayesian optimisation
Exploration
PAGE 81
What molecule provides maximum
information to the model?
2. Generative design
Exploitation
New molecules
generated
Deep
Learning
2
Score
Multi-objective optimization
Policy gradient reinforces to deliver optimal solution
Optimising features with Evotec's molecular design apps
Fit-for-Purpose application of tools to drive project success
Project MPO Score
5.5
5
45
3.5
0
1
2
20
3
40
6 months
(intermediate goal met)
Early lead
Project MPO score vs Sequential ID
60
A pre-clinical drug candidate in 12 months and < 150 compounds
80
Sequential Compound ID
100
4
pre-candidates identified for
downstream profiling
120
12 months
SEN CRECIO
140
3. Quantum mechanics
|
Ki 460 nM
Y.
Global Model
FMO-based
SBDD
4. Machine learning DMPK
Project Models
EVO PPS HUMAN (RFR)
EVO PPB RAT (RFR)
EVO LOGO (RR)
EVO MICS HUMAN (RFC)
EVO MICS RAT (RFC)
EVO MICS MOUSE (RFC)
EVO PPB MOUSE (RFR)
EVO SOLUBILITY (RFC)
EVO HERG IRFRI
EVO HERG CLASS (RFC)
EVO CACO2 (FR)
Your Models
Shared Models
HERG
112
Ki 4.1 nM
780
371
Model Performance-fold strated cross vadation)View entire presentation