Overview
Generate molecules that:- Fit precisely into protein binding pockets
- Optimize multiple properties simultaneously
- Explore novel chemical scaffolds
- Satisfy drug-likeness criteria
- Are synthetically accessible
Design Approaches
Pocket-Based Design
Generate molecules to fill a specific binding pocket. Best for structure-based design.
Scaffold Hopping
Create chemically distinct scaffolds with similar activity. Expand chemical diversity.
Fragment Growing
Start with a fragment and grow into full ligand. Fragment-based drug design.
Lead Optimization
Optimize existing compounds for improved properties while maintaining activity.
Quick Start: Generate Molecules for a Pocket
Define Your Target
Upload protein structure or use LiteFold prediction. Binding pocket identified automatically.
Configure Generation
Set parameters:
- Number of molecules (10-1000)
- Molecular weight range
- Drug-likeness filters
- Diversity settings
Select Generation Model
- DiffSBDD: Diffusion model for structure-based design
- TargetDiff: Optimized for druggable pockets
- Pocket2Mol: Graph-based generation
Generate Molecules
Click “Generate”. LiteFold creates molecules in 10-30 minutes depending on count.
Review and Filter
- Visual inspection of top molecules
- Docking scores
- ADMET predictions
- Synthetic accessibility
Generative Models
Diffusion Models
How they work: Gradually denoise random molecular structures into valid, pocket-fitted molecules. Models:- DiffSBDD: General structure-based design
- TargetDiff: Enhanced for drug-like molecules
- DiffLinker: Links molecular fragments
- Exploring diverse chemical space
- Novel scaffolds
- Complex pocket geometries
Graph Neural Networks
How they work: Build molecules atom-by-atom or fragment-by-fragment using graph representations. Models:- Pocket2Mol: Pocket-conditioned generation
- GraphGA: Genetic algorithm with GNN scoring
- Fragment-based design
- Scaffold decoration
- Specific chemistry constraints
Transformer Models
How they work: Generate SMILES strings using language model approaches. Models:- REINVENT: Reinforcement learning optimization
- ChemFormer: Pre-trained transformer
- Multi-objective optimization
- Fine-tuning on custom data
- Large-scale generation
Design Workflows
Structure-Based De Novo Design
Start with protein structure, generate optimized ligands.Pocket Analysis
LiteFold analyzes binding pocket:
- Volume and shape
- Hydrophobic/hydrophilic regions
- Key interaction sites (H-bond donors/acceptors)
- Subpocket identification
Generation Constraints
Specify:
- Molecular weight: 250-500 Da
- LogP: 0-5
- Required interactions (e.g., H-bond to Asp855)
- Forbidden substructures (e.g., PAINS)
Scoring and Filtering
LiteFold automatically:
- Docks all molecules
- Predicts ADMET
- Calculates synthetic accessibility
- Ranks by multi-objective score
Fragment-Based Design
Start with fragment hits, grow into lead-like molecules.Growing Strategy
Choose:
- Greedy growing: Optimize affinity at each step
- De novo linking: Connect fragments with linkers
- Decoration: Add substituents to core scaffold
Scaffold Hopping
Find chemically distinct scaffolds with similar binding.Define Pharmacophore
Extract key features:
- H-bond donors/acceptors
- Hydrophobic centers
- Aromatic rings
- Charge centers
Generate Alternatives
LiteFold creates molecules matching pharmacophore but with different scaffolds.
Lead Optimization
Optimize existing lead compound for better properties.Define Optimization Goals
Select properties to optimize:
- ↑ Binding affinity
- ↑ Solubility
- ↓ CYP inhibition
- ↑ Brain penetration
- ↓ Molecular weight
Multi-Objective Optimization
LiteFold uses reinforcement learning to optimize multiple objectives simultaneously.
Design Constraints
Drug-Likeness Filters
Apply standard filters:- Lipinski’s Rule of 5: MW ≤ 500, LogP ≤ 5, HBD ≤ 5, HBA ≤ 10
- Veber Rules: Rotatable bonds ≤ 10, TPSA ≤ 140
- Lead-like: MW ≤ 350, LogP ≤ 3.5
- Fragment-like: MW ≤ 250, rotatable bonds ≤ 3
Custom Constraints
Define your own:- Required substructures: Force inclusion of specific groups
- Forbidden substructures: Exclude toxicophores, PAINS
- Specific interactions: Require H-bond to Asp855
- Property ranges: LogP 2-4, MW 300-450
Chemical Space Restrictions
- Allowed reactions: Limit to high-yielding chemistry
- Available building blocks: Use in-stock reagents
- Synthetic routes: Prefer ≤ 5 step syntheses
- Stereochemistry: Control chiral centers
Evaluation Metrics
Docking Score
Predicted binding affinity from molecular docking.Synthetic Accessibility (SA Score)
Estimates synthesis difficulty (1-10):- 1-3: Easy to synthesize
- 4-6: Moderate difficulty
- 7-10: Very difficult or impractical
QED (Quantitative Estimate of Drug-likeness)
Overall drug-likeness score (0-1):- > 0.67: Drug-like
- 0.49-0.67: Moderate
- < 0.49: Non-drug-like
ADMET Predictions
- Absorption: Caco-2 permeability, HIA
- Distribution: LogD, plasma protein binding
- Metabolism: CYP substrate/inhibitor
- Excretion: Clearance
- Toxicity: hERG, Ames, hepatotoxicity
Novelty Score
Measures chemical novelty vs. known compounds:- Tanimoto similarity to nearest ChEMBL compound
- Scaffold novelty: Is core structure new?
Post-Generation Workflow
Visual Inspection
Manually review binding modes:
- Do interactions make sense?
- Any steric clashes?
- Favorable interactions captured?
Synthesis Planning
For top 20, generate synthetic routes. Prioritize by:
- Synthetic accessibility
- Building block availability
- Number of steps
Example: Designing EGFR Inhibitors
Let’s design novel EGFR kinase inhibitors:Target Preparation
- Protein: EGFR with T790M resistance mutation
- Binding site: ATP pocket
- Known inhibitors: Erlotinib (resistant), osimertinib (active)
Design Strategy
Generate molecules that:
- Fit in T790M mutant pocket
- Avoid steric clash with Met790
- Maintain key H-bonds (Met793, Cys797)
Generation
- Model: DiffSBDD
- Count: 500 molecules
- Constraints: MW 300-500, LogP < 5, covalent to Cys797
- Time: 25 minutes
Filtering
- Drug-like: 387/500 pass
- ADMET: 241/387 favorable
- SA score < 6: 156/241
- Docking score < -9: 43/156
Top Candidates
- Cluster into 12 scaffolds
- Select 2 from each scaffold (24 total)
- Visual inspection: 18 chemically sensible
- Synthesis planning: 10 feasible (≤ 5 steps)
Advanced Features
Conditional Generation
Generate molecules conditioned on:- Activity: Generate only high-affinity binders
- Selectivity: Active on target, inactive on off-target
- Properties: Optimize for BBB penetration, oral bioavailability
Multi-Target Design
Generate molecules active against multiple targets:- Dual inhibitors (e.g., EGFR + HER2)
- Polypharmacology
- Avoiding anti-targets (e.g., hERG)
Generative Optimization
Iterative design cycles:- Generate molecules
- Evaluate (dock, predict properties)
- Retrain model on best molecules
- Generate improved next generation
- Repeat
Integration with Synthesis Planning
Top candidates automatically flow to synthesis planning:- Retrosynthesis: Identify synthetic routes
- Building block search: Check commercial availability
- Route scoring: Rank by feasibility and cost
- Step-by-step protocols: Reaction conditions
Best Practices
Limitations
Current de novo design limitations:- Synthetic feasibility: Models may suggest hard-to-make molecules
- Activity prediction accuracy: In silico predictions need experimental validation
- Scaffold bias: Models may favor overrepresented scaffolds in training data
- Specificity: Hard to guarantee selectivity vs. off-targets
- ADMET prediction errors: Some toxicities hard to predict computationally
Next Steps
Molecular Docking
Validate generated molecules with docking
Molecular Dynamics
Confirm binding stability with MD
Drug Discovery Workflow
Integrate de novo design in full campaigns
Compound Screening
Combine with virtual screening for comprehensive coverage