Knowledge-based

InfoChem GmbH Chemnotia AB

Fernando F. Huerta

powered by

Compound Design

QSAR models

Ligand Based

Pharmacophore  models  

Structure Based

HTS

Compound (Drug) Design

Fragment Based

How Drugs Look?

N

O

OH

ONH2F

FN

HNN

O

OH

ONH2F

FN

HNN

O

OH

ONH2F

FN

HNN

O

OH

OF

FN

HN

Norfloxacin  (Noroxin™)  

Ciprofloxacin  (Cipro™)  

Sparfloxacin  (Zagam™)  

Grepafloxacin  (Raxar™)  

Drug Discovery Today 2011, 16, 722 Drug Discovery Today 2011, 16, 779

N

HN

SO

NO

O

N

HN

SO

N

O

O

N

HN

SO

N

O O

OF

F

Nexium®   Rabeprazole®   PANTOPRAZOLE®  

O

NN

N

Cl S

NN

N O

HO

Seroquel®  Loxapine®  

Protein  

Binding  

Site  

Why Do Drugs Look Similar?

Activity, Selectivity, ADMET,..

Similar structural motif share similar properties(?)

Drug Design Paradigm

What would you do?

Compound (Drug) Design

Knowledge-based Compound Design

Transforms from Reaction Databases

Reaction database e.g. SPRESI

proprietary databases commercial databases

Transform library

1. Pre-processing

Automatic transform extraction

(ICMAP/CLASSIFY)

N

Remove bond 1-3Make new single bond between atoms 3 and 5Remove bond 1-2

Make new single bond between atoms 3 and 4

12

3

O4

5

6

7 8

910

R111

R212

13

14

Template (2 levels):

Transform:

Make new single bond between atoms 2 and 6.

Decrease bond order of double bond 2=4 by 1.

Add group to atom 3: -OH

Remove bond between atoms 3 and 6.

2. Retrosynthesis

Lookup stored

example reactions

Target molecule

O

O

HH

Precursor(s) 1

... Precursor(s) 2

Precursor(s) n ...

Transform x

Transform y

Transform z

Transforms from Reaction Databases

Reaction database e.g. SPRESI

proprietary databases commercial databases

Transform library

2. Forward Reaction

Lookup stored

example reactions

Starting Material or Reactant

Transform x

Transform y

Transform z

Product(s) 1

... Product(s) 2

Product(s) n ...

N

Cl N

O

Retrosynthetic Analysis

•  Target orientated •  Complexity reduction •  Availability of starting

materials •  Multistep process

Reaction Prediction

•  Unknown product molecules

•  Molecular size •  Reagents •  Single reaction step

Forward Reaction Prediction (cont.)

•  Number of transforms

O

S

OHO O

N

OH

H H

NNH2

ICSYNTH Strategy Parameters

•  Rating of generated precursors/products

•  Strategy: defined by a set of parameters

•  42 parameters (Retrosynthesis / FRP)

O OH

Reactant

Transform

Product(s)

ICSYNTH FRP Parameters

Strategy Parameters Optimization

42 parameters (reactant, transform, product)

optimization algorithm

Reactivity Mapping

FG Library Design

Ring Introduction

Scaffold Modification: Not Scaffold Hopping!!

ICSYNTH FRP Strategies

FRP Examples / Applications

Reactivity Mapping

N NH

N

N OO

N NH

N

N OO

R

N NH

N

N NO R

N NH

N

N OO

Hal

N N

N

N OO

ON NH

N

N OO

R

FRP Examples / Applications

FG Library Design

N NH

N

OHO

reaction center

Knowledge-based Compound Design

Other Databases

Knowledge-based Drug Design

•  One synthetic step (reliable?) •  Novelty •  Drug like

•  Physicochemical properties •  Lipinski

HN

O N

S

OHO

O

Penicillin G

Example  from  J.  Chem.  Inf.  Model.,  Vol.  49,  No.  5,  2009,  1163-­‐1184  

Knowledge-based Drug Design inspired by “de novo design using reaction vectors”

Example 1

HN

O N

S

OHO

O

Penicillin G

Example  from  J.  Chem.  Inf.  Model.,  Vol.  49,  No.  5,  2009,  1163-­‐1184  

HN

O N

S

OHO

O

Reaction Vectors

ICSYNTH FRP (precision Medium) ICSYNTH FRP (precision High)

Knowledge-based Drug Design inspired by “de novo design using reaction vectors”

Example 1

Knowledge-based Drug Design Example 1

•  30 suggested cpds from ICSYNTH vs 1000 cpds (Reaxys, penicillinG sss)

•  Fingerprint similarities calculated (30000) •  Identical compounds filtered off (similarity = 1) •  Compounds with Tanimoto distance between 0.5-0.9

were selected •  Synthetic data (ICSYNTH) available •  Calculated med chem properties of new suggestions as

well as med chem properties of previously reported ones available for analysis

Knowledge-based Drug Design Example 1

Example 2

N

Cl N

O

Knowledge-based Drug Design

Core Structure (Diazepam)

Reaxys search

214 reactions / products

N

Cl

F

N

ON

Cl

HOO

N

O

N OO

N

ON

Cl O

NHO

NH

N O

NCl…

100 suggestions

Example 2

Knowledge-based Drug Design Processing Information

•  80 new reactions identified

•  10 Identical matches

•  8 suggested products with a Tanimoto

distance <0.2

•  2 abstraction errors (knime)

but… it’s not enough for compound design…

Example 2 (part II)

N

Cl N

O

Knowledge-based Drug Design

Core Structure (Diazepam)

80 new suggested reactions/products

versus

Reaxys substructure search

2564 related compounds

•  Fingerprints calculated

•  Identical compounds filtered off (Tanimoto = 1)

•  13 suggested products with a Tanimoto 0.7-0.9

•  Physicochemical properties calculated and compared

Knowledge-based Drug Design Example 2 (part II)

New “suggested” molecules show similar properties to the known-ones

Cl

O

NN

N

Cl

O

HNO

Intermediate* used for ICSynth FRP and Reaxys sss

* Not real precursor for the final molecule

•  61 suggestions ICSYNTH FRP (one synthetic step away from intermediate)

•  922 related compounds in the literature (Loxapine included)

•  6 new compounds with Tanimoto distance between 0.5-0.9 were suggested

Loxapine®

Knowledge-based Drug Design Example 3

O

N

R

Cl O

NN

Cl

ArZ

O

NNH

Cl

Ar

O

NX

Cl

One more thing…

building synthetic confidence

filtering ICSYNTH cpds with low synthetic background •  number of precedent reactions of the same type •  precedent yield reported •  level of similarity from the original reaction

Summary

ACKNOWLEDGMENTS

InfoChem Peter Loew Heinz Saller Christoph Oppawsky Mike Hutchings Hans Kraut Valentina Eigner-Pitto Josef Eiblmaier Ulf Frieske Stephanie North

Chemnotia Anders Bogevig Tobias Rein

THANK YOU