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T H I S A C S W E B I N A R W I L L B E G I N S H O R T L Y . . .

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Effective Exploration of Chemical Space in Hit-Finding

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This ACS Webinar is co-produced with the ACS Division of Medicinal Chemistry, the American Association of Pharmaceutical Scientists, and the Journal of Chemical Information and Modeling

Zoe CourniaInvestigator – Assistant Professor Level,

Biomedical Research Foundation, Academy of Athens and JCIM Associate Editor

Johanna “Hanneke” JansenDirector, Novartis Institutes for

BioMedical Research


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Global Discovery Chemistry

Chemical space is vast

Image courtesy Peter Ertl, Novartis (2013)


Hit-Finding: Assess a selection from chemical space in your assay

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Every well counts ....

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Kingston Black is a cultivar of apple mostly used for?

Audience Survey QuestionANSWER THE QUESTION ON BLUE SCREEN IN ONE MOMENT

• Apple pie

• Apple sauce

• Snacking

• Apple Cider

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* If your answer differs greatly from the choices above tell us in the chat!

Creative Commons photo by Andy Roberts


Quality means different things for different projects

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• Keep structure

Apple pie

• Cook up smooth

Apple sauce

• Packable

Hiking snack

• Acidity and tannins

Apple cider

Highest quality apple?

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Quality means different things for

different projects: hit-finding

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• Non-cytotoxic

Cellular assay

• Solubility

Fragment-screen

• No fluorophores

Fluorescent read-out

• Novelty

MedChem starting point


• Consider exclusions & bias

– Do not include compounds that are

incompatible with quality & purpose

– Select highest quality compounds as

representatives (highest likelihood of

success)

– Availability of privileged core set?

(computational model, related hit-list)

• Balance quality with diversity to

cover chemical space

– Opportunity to discover novel mechanisms or

chemical matter outside of current domain

Articulation of quality as it relates

to purpose drives selection strategy

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Biased Complement Diversity, BCD

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• Three internal case studies show that BCD works

– Bacteriology: Increased proportion of non-cytotoxic hits from 31% to 62%

– Two target-driven hit-finding campaigns identified quality starting points by

complementing knowledge-driven subsets with biased diversity sets


Schematic representation of BCD

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Create compound library

• Available structures

• Chemical space: clustering of 2D fingerprints

• Quality attributes: Class A-D & exclusions

Loop over every cluster & assess quality

• Keep all singletons

• If all-D: keep single best

• Else: 3-tier biased selection

Biased selection

• User-defined coverage number

• RDKit Diversity Picker KNIME node

• Explicit complement: class-A is core-set (selected completely)

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KNIME workflow with multiple

meta-nodes for performing the selection

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Time to run Malaria case study on Windows laptop (picking 50k from 300k molecules)

1 hour 20 minutes with predefined cluster-list (as deposited in SI)

3 hours when including clustering (ICM MolCluster node)


• Input 1: Structure input– Available structures (after exclusions)

– Cluster information

– Classification: class A-D

• Input 2: Five variables– Core-set?

– Sampling density

– Approach to pick single-best from all-D clusters

• Output: Identifiers of the selected subset

• Dedicate brainpower to decide and/or develop core-set(s) and quality attributes

Input and output nodes / notes

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Antibiotic drug discovery is more difficult for Gram-negative bacteria compared to Gram-positive bacteria mostly because of?


• Efflux

• The presence of a second, outer membrane

• Generic competition

• Resistance

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Bacteriology case study: Optimize quality of hits that target Gram(-) pathogens

• Define quality: reduced cytotoxicity against mammalian cells

• Translate quality to attributes for classification: Analyze relationship between Gram(-)

activity, clogD, and cytotoxicity (in-house data)

– 87% of compounds with activity < 20 M in Gram(-) and a clogD > 3 are cytotoxic vs mammalian

cells

• Classification: Assign all compounds with clogD > 3 to class D

– Sliding scale bias for MW, ROT, PSA, HBD from class A → C

– Additional bias using substructure flags

• Success criterion: Increase proportion of confirmed hits that are free of cytotoxicity

– Historical proportion is 31% for compounds with EC50 < 20 M in Gram(-)

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Bacteriology BCD design increases

the proportion of non-cytotoxic hits

• Confirmed hits: EC50 < 20 M in ≥ 1 WT Gram(-) strain and no cytotoxicity

against mammalian cells

– Historical proportion non-cytotoxic hits: 310/985 = 31%

– BCD proportion non-cytotoxic hits: 40/65 = 62% = success !

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Source set Screening set # Confirmed hits

832k 76k 40

Color by

class

A

B

C

D


Perspective on using overall hit-rate

to assess success: DON’T !

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Class

# Compounds in

screening set

Class % in

screening set # Primary hits Hit-rate

A 205,822 26 2,820 1.4

B 158,285 20 3,258 2.1

C 81,032 10 2,570 3.2

D 344,481 44 34,047 9.9

Total 789,620 42,695 5.4

Class

# Compounds in

screening set

Class % in

screening set # Primary hits Hit-rate

A 50,206 71 859 1.7

B 10,216 14 254 2.5

C 1,611 2 73 4.5

D 8,871 13 813 9.2

Total 70,904 1,999 2.8

Comparable full deck data-set (bacteriology)

BCD selection (bacteriology)

Hit-rate is not constant

across classes

BCD samples more from

lower hit-rate class

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BCD increases the number of high quality hits

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Unbiased diversity* BCD

Class

Class

% in set # Primary hits Hit-rate

Class %

in set

# Primary

hits Hit-rate

A 27 311 1.6 71 859 1.7

B 19 284 2.1 14 254 2.5

C 8 199 3.4 2 73 4.5

D 46 3,395 10.3 13 813 9.2

Overall 4,189 5.8 1,999 2.8

*Very similar numbers in the full-deck dataset, and in a 71k random selection


Case-studies exemplify real life

challenges and BCD opportunities

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Case study Core set(s) Objective

Bacteriology None

Gram-negative antibacterial

hits without mammalian

cytotoxicity

Target-1 DockingNovel starting points for

chemistry optimization

Target-2

Prior hits, privileged

scaffolds,

pre-plated diversity

Chemical matter for a new

MOA

Malaria Similarity [example]

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• Quality determinant: chemotype novelty

• Triage: dose-response curve shape, analytical QC, ligand efficiency,

dissimilar to known chemotypes

Target-1: Novel starting points for chemistry optimization

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Color by

class

A

B

C

D

Source set Screening sets # Confirmed hits # Quality hits

1,157k BCD: 53k 119 3

Docking: 51k 293 7


• Quality determinant: MOA validation

• Triage: Assay interference; orthogonal biophysical assays

Target-2: Chemical matter for a new MOA

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Color by

class

A

B

C

D

Source set Screening sets # Confirmed hits # Quality hits

736k BCD: 19k 81 5

Prior hits: 506 10 4

privileged

scaffolds: 15k 68 3

pre-plated

diversity: 18k 73 2

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Malaria: Public domain data set to exemplify the workflow

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Complete

literature hit list

Source set Screening sets # Confirmed hits

306k BCD: 50k 31

Similarity: 476 5

Color by

class

A

B

C

D

• Quality determinant: [generic profile with publicly available attributes]

• Triage: [comparison with published set of 172 “confirmed and cross-

validated hits”]


We’re finding good quality confirmed hits – Effective exploration!

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Case study Core set(s) ObjectiveScreening set / Conf.

hits / Quality hits

Bacteriology NoneGram-negative antibacterial hits

without mammalian cytotoxicity76k / 40

Target-1 DockingNovel starting points for chemistry

optimization104k / 412 / 10

Target-2

Prior hits, privileged

scaffolds,

pre-plated diversity

Chemical matter for a new MOA 52k / 232 / 14

Malaria Similarity [example] 50k / 36

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Which of the following are representations of Chemical Space?


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1.

4.

3.2.


Assessing chemical space coverage when applying BCD

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BCD is designed for coverage: Clustering and complementation

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• “Biased selection” meta-node uses the complement function of “RDKit

Diversity Picker” in every round


• Cluster 26466 has 55 compounds: 3 B, 50 C, 2 D

• Coverage settings: Ceiling ( 0.5*√(53) )= 4

• All three B compounds get selected

Malaria case study example: The impact of complementation

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Task: Pick one C from set of 50 to complete selection

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There are class-C compounds that are

close to class-B in property space

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A: Complement set (similarity)

B: 250 ≤ MW ≤ 400; -1 ≤ SlogP ≤ 3; ROT≤ 5 & no substructure match

C: 200 ≤ MW ≤ 600; -1 ≤ SlogP ≤ 5; ROT≤ 10 & substructure “Flag”

D: MW > 0

... and they are similarly inactive


The 7 true active compounds from this

cluster are dissimilar in property space

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Complement design picks one of

these, which also is one of the

confirmed hits !

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Scaffold-based coverage assessment:

Two different scaffold definitions

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a) Bemis & Murcko, J. Med. Chem. 1996, 39, 2887-2893b) Schuffenhauer et al, J. Chem. Inf. Model. 2007, 47, 47-58


Scaffold-based coverage assessment:

Good diversity & complementation

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Compound set # Conf. hits

# BM

scaffolds in

conf. hitsb

# Conf. hits with

level-3 scaffold

# Level-3

scaffolds in

conf. hitsb

Bacteriology

BCD 40 37 25 21

Target-1

Docking 293 217 (4) 159 137 (2)

BCD 119 108 (4) 50 50 (2)

Target-2

Pre-plated diversity 73a 72 61 59

Prior hits 10 10 10 10 (1)

Privileged scaffolds 68 64 (1) 66 56 (1)

BCD 81 81 (1) 76 76 (2)

Malaria

Similarity 5 2 5 2

BCD 31 30 (11) 24 22 (11)

All other confirmed hits 136 67 (11) 125 50 (11)a) 72 have a BM scaffoldb) In parentheses is number of scaffolds shared with another subset

Confirmed hit

BM scaffold

Level-3 scaffold

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2D maps of confirmed hits in chemical

space show good complementation

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Target focus core: docking, prior hits, similarity

Other core: pre-plated diversity

Other core: privileged scaffolds

BCD

Not selected

Optibrium’s StarDrop Chemical Space tool

(visual clustering approach: t-SNE)

Target 1 Target 2 Malaria


Biased complement diversity selection for effective

exploration of chemical space in hit-finding

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BCD workflow selects high quality representatives to cover a given chemical space

o Increased proportion of non-cytotoxic hits from 31% to 62% in bacteriology

o Two target-driven hit-finding campaigns identified quality starting points by

complementing knowledge-driven subsets with biased diversity sets

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• Co-authors: Gianfranco De Pascale, Susan Fong, Mika Lindvall, Heinz Moser, Keith Pfister, Bob Warne, and Charles Wartchow

• Complement design: Eric Martin

• Data & workflow infrastructure: Heather Hogg, Manuel Schwarze

• Antibiotic property space: Folkert Reck

• Chemical space: Peter Ertl, Ansgar Schuffenhauer

• Reviewers of the paper (internal & external)

• All coworkers past & present for sharing and creating drug discovery “quality” insights

Acknowledgements

47



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BioMedical Research


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Celebrating 5 years & 50 Drug Discovery Webinars!http://bit.ly/acsDrugDiscoveryArchive

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2014 2015 2016 2017 2018

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http://bit.ly/acsDrugDiscoveryArchive


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Women in Computational Chemistry

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• Platform for us to disseminate seminal works • Set inspiring examples of female role models• Create a network of peers that will instigate change for the new

generation of Women in Science in the field of Computational Chemistry

Coming out in May 2019!




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BioMedical Research



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Be a featured fan on an upcoming webinar! Write to us @ [email protected] 53

“All things around ACS Webinars are benefits! We learn about great research topics, we take advantage of our Membership, and we are being friendly to the environment because we can connect from anywhere.”

Jans Alzate-MoralesPharmaceutical Chemist, Associate Professor, and Director of Ph.D Program in Sciences Mention in Modeling of Chemical and Biological Systems, Centre for Bioinformatics and Molecular Simulations (CBSM), Universidad de TalcaACS member for 9 years strong!

[email protected]

54https://www.aaps.org/education-and-research/workshops/brain-barriers

4/17/2019

28

55Find out more about the ACS MEDI Division! www.acsmedchem.org

Join the Division Today!

For $25 ($10 for students), You Will Receive:

• A free digital copy of our annual medicinal chemistry review volume (over 680 pages, $160 retail price)

• Abstracts of MEDI programming at national meetings

• Access to student travel grants and fellowships

56


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@AmerChemSociety

https://www.linkedin.com/company/american-chemical-society

@AmerChemSociety

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Benefits of ACS Membership

Chemical & Engineering News (C&EN) The preeminent weekly digital and print news source.

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NEW! ACS Career Navigator Your source for leadership development, professional education, career services, and much more.

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