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Hit to Lead Discovery of Benzylpiperidine Acetylcholinesterase Inhibitors Using Generative Models: a Retrospective Case Study, Elix, CBI 2022

Elix
October 26, 2022

Hit to Lead Discovery of Benzylpiperidine Acetylcholinesterase Inhibitors Using Generative Models: a Retrospective Case Study, Elix, CBI 2022

Elix

October 26, 2022
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  1. Hit to Lead Discovery of Benzylpiperidine
    Acetylcholinesterase Inhibitors Using Generative
    Models: a Retrospective Case Study
    Nazim Medzhidov, Ph.D & Joshua Owoyemi, Ph.D
    Elix, Inc.
    Chem-Bio Informatics Society (CBI) Annual Meeting 2022, Tokyo, Japan | October 26th, 2022

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  2. 2
    Background
    ● Challenges associated with the traditional drug discovery process have facilitated the application of machine
    learning approaches in this domain.
    ● Generative AI approaches for molecular design are actively investigated.
    ● Evaluating generative models in silico is challenging, confirmation requires experimental validation (expensive)
    ● Majority evaluate model performance based on optimizing computable properties (logP, QED, SA score, etc.)
    ● How to select generated candidates efficiently?
    Objectives
    ★ Design a scenario and a
    pipeline to evaluate generative
    models in silico:
    ○ Hit-to-lead campaign
    ○ Novel chemotype discovery
    ★ Test our Elix Discovery™
    Platform
    ★ Candidate prioritization
    pipeline

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  3. 3
    Study Workflow
    AI Model
    Development
    Dataset Preparation
    Post-processing &
    Prioritization
    Result Analysis
    ● Pre-training dataset:
    ○ ChEMBL
    ○ AChE inhibitors
    removed
    ○ Target chemotype
    scaffold removed
    ● Training Set:
    ○ AChE inhibitors from
    ChEMBL
    ● Elix Discovery™ Platform
    ● Elix Predict:
    ○ AChE inhibitory activity
    prediction model
    ○ Blood Brain Barrier (BBB)
    Permeability prediction
    model
    ● Elix Create:
    ○ SmilesFormer Generative
    Model
    ○ 10 sampling runs
    ● 30K molecules generated in
    each of 10 sampling runs
    ● Post-processing:
    ○ Phys-Chem Filters (RO5)
    ○ MCF filters
    ○ Novelty
    ○ BBB Permeability
    ● Prioritization:
    ○ QED score
    ○ Predicted activity
    ○ Binding affinity (docking)
    ● Quality assessment:
    ○ Target scaffold discovery
    ○ Documented potent compound discovery
    ● Short list of best 200 molecules from each
    run
    ● Final short-list of 20 most frequently
    selected best compounds

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  4. 4
    Dataset Preparation

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  5. 5
    Datasets: Acetylcholinesterase inhibitors
    ChEMBL (~2.2M)
    Training dataset (1076): A + B + C
    AChE Inhibitors with IC50 values (4,238)
    AChE inhibitors
    before 1992 (120)
    More recent molecules with same
    chemotypes present in A (847)
    A
    Pre-training dataset (~2.2M):
    Physostigmine
    Tacrine Rivastigmine
    B
    C
    Hit & hit expansion
    compounds (109)
    D
    Molecules containing piperazine,
    piperidine or indan (357) (Hidden)
    Established chemotypes before 1992
    AChE inhibitors
    removed (15.5K mols)
    48 mols with the
    scaffold removed
    B
    1992
    First appearance of
    donepezil chemotype
    in ChEMBL database
    A B
    C D
    (Hit compound) (Target chemotype)
    A

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  6. 6
    Datasets: Chemical Space Visualization
    Physostigmine
    Tacrine
    Rivastigmine
    Hit Compound
    Target
    Chemotype

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  7. AI Model Development
    7

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  8. 8
    Elix Discovery™ Platform
    Generative Model
    ● SmilesFormer
    ○ Pre-trained on ChEMBL dataset without AChE
    inhibitors and target scaffold
    ○ Trained on: datasets A + B + C (1076 samples)
    ● Multiobjective Optimization Problem:
    ● SA score
    ● QED score
    ● Favorable physical-chemical properties
    ● Novelty (distance from the training set)
    ● Activity
    Predictive Models
    ● AChE inhibitory activity prediction model:
    ○ GCN
    ○ Trained on: datasets A + B + C (1076 samples)
    ● Blood Brain Barrier (BBB) Permeability prediction model:
    ○ GCN
    ○ Trained on an in-house dataset (9059 samples)

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  9. Molecule Generation,
    Post Processing &
    Prioritization
    9

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  10. 10
    Generation strategy and post-processing pipeline
    30K mols /
    run
    Run 1
    Run 2
    Run 5
    Run 3
    Run 4
    Run 6
    Run 7
    Run 10
    Run 8
    Run 9
    Random Sampling
    One seed
    Group seed
    No seed
    1
    2
    3
    Filtering
    5
    6
    ~4000 mols /
    run
    ● RO5
    ● MCF
    ● Novelty
    ● BBB
    Permeability
    Run 1
    Run 2
    Run 5
    Run 3
    Run 4
    Run 6
    Run 7
    Run 10
    Run 8
    Run 9
    Prioritizing
    5
    6
    ● QED
    ● Predicted
    activity
    ● Binding
    affinity
    (docking)
    200 mols /
    run
    Run 1
    Run 2
    Run 5
    Run 3
    Run 4
    Run 6
    Run 7
    Run 10
    Run 8
    Run 9
    Aggregation
    20 most
    frequently
    selected
    candidates
    ● Recommendation
    score:
    ○ Consistency of
    selection
    ○ Min = 1, Max = 10

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  11. 12
    Discovering reported potent scaffold and molecules
    Reported scaffold
    discovery success
    (number of runs)
    Reported potent compound
    discovery success
    (number of runs)
    No Seed 0 / 10 0 / 10
    One Seed 4 / 10 5 / 10
    Group Seed 9 / 10 9 / 10
    D
    Molecules from hidden dataset D (target
    chemotype) containing represented substructures
    Reported potent scaffold
    A
    B
    Molecules rediscovered with One Seed setting Molecules rediscovered with Group Seed setting
    IC50 = 81 nM
    Rank = 8
    IC50 = 6.7 nM
    Rank = 31
    IC50 = 58 nM
    Rank = 107
    IC50 = 94 nM
    Rank = 166
    IC50 = 81 nM
    Rank = 56
    IC50 = 30 nM
    Rank = 71
    IC50 = 6.7 nM
    Rank = 124
    IC50 = 94 nM
    Rank = 393
    Random Sampling

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  12. 13
    Final 20 Candidates by Recommendation Score
    No seed One seed
    Legend: top 1% recommendation score (max = 10)

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  13. 14
    Conclusion
    ● Designed a retrospective case study of novel chemotype discovery for generative models (quality assessment)
    ● Tested our Elix Discovery™ platform in a hit-to-lead discovery campaign
    ● Given an early hit compound, optimized the scaffold to a more complex diverse scaffolds including a reported
    potent indanone-piperidine scaffold
    ● Multiple sampling runs and recommendations score analysis helped to focus on consistently top ranked
    candidates
    ● Among the prioritized candidates, reported indanone-piperidine containing potent molecules were discovered
    ● These molecules were included in the top 1% of the generated molecules
    ● Final 20 top ranked candidates included at least one known potent AChE inhibitor
    ● Potential presence of yet unknown potent compounds among final recommendations

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  14. www.elix-inc.com

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  15. 17
    Final 20 candidates: One seed vs Group Seed
    Group seed
    One seed

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  16. 18
    Chemical Space Visualization
    Physostigmine
    Tacrine
    Rivastigmine
    Hit Compound
    Target
    Chemotype

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