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

Transcript

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
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    Run 5
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    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. Results
    11
    

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

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14. 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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15. www.elix-inc.com
    

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16. APPENDIX
    16
    

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

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

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