REINVENT Quickstart

You want to generate optimized molecules toward a target property profile: pick a REINVENT run type, set your generation and scoring parameters, and submit — the fastest path is a Sampling run, which generates diverse compounds straight from the prior model with no scaffold required.

Webapp input methods

The .bloom* webapp provides a streamlined interface for REINVENT de novo molecular design.

Run type selection

Scaffold source

From previous job

• Job Output Selector: Browse completed jobs with compound outputs
• SMILES extraction: Automatic extraction from job outputs
• Compatible tools: ZINC search, PubChem lookup, REINVENT outputs

Webapp parameters

Scaffold configuration (when applicable)

• Scaffold SMILES: Comma-separated list of scaffold structures
• Required for: Scoring runs; optional for sampling runs
• Format: Standard SMILES notation

Generation parameters

Scoring configuration

Custom scoring (when selected)

• Diversity Filter (JSON): Custom diversity filter configuration
• Scoring Function (JSON): Custom scoring function with components and weights

Job options

• Job Name (Optional): Custom identifier (default: Denovo REINVENT (timestamp))
• Use Personalized Settings: Toggle for project-specific configuration from Supabase

Workflow integration

Sampling + Scoring confirmation

When selecting "Sampling + Scoring" run type, a confirmation dialog appears:

• Shows number of molecules to generate
• Confirms the combined workflow
• Allows cancellation before proceeding

Continue to scoring

After a sampling run completes:

• "Continue to Scoring with Sampling Results" button appears
• Automatically uses sampling output URI
• Seamlessly chains sampling → scoring workflow

Generative molecule design with REINVENT in the platform webapp

REINVENT 4 is AstraZeneca's modern open-source generative AI framework for molecular design, published in the Journal of Cheminformatics (2024). It uses recurrent neural networks and transformer architectures with reinforcement learning (RL) to generate optimized molecules compliant with user-defined property profiles.

Citations

Loeffler HH, He J, Tibo A, Janet JP, Voronov A, Mervin LH, Engkvist O. Reinvent 4: Modern AI–driven generative molecule design. Journal of Cheminformatics. 2024;16:20. doi: 10.1186/s13321-024-00812-5

Blaschke T, Arús-Pous J, Chen H, Margreitter C, Tyrchan C, Engkvist O, Papadopoulos K, Patronov A. REINVENT 2.0: An AI Tool for De Novo Drug Design. Journal of Chemical Information and Modeling. 2020 Dec 28;60(12):5918-5922. doi: 10.1021/acs.jcim.0c00915

Key capabilities

• De novo design: Create molecules from scratch
• Scaffold hopping: Generate novel scaffolds maintaining activity
• R-group replacement: Optimize substituents systematically
• Linker design: Connect molecular fragments
• Molecule optimization: Multi-objective property optimization
• Library design: Generate focused compound libraries

Available at https://github.com/MolecularAI/REINVENT4 under Apache 2.0 license.

Step-by-step molecule generation workflow

Common generative design scenarios

Property optimization campaigns

1. Define target property profile (logP, MW, TPSA ranges)
2. Select appropriate generative model and scoring functions
3. Run optimization for 50-200 epochs
4. Analyze property distributions and select top candidates
5. Export optimized compound library for further evaluation

Scaffold hopping studies

1. Provide starting scaffold or known active compound
2. Set similarity constraints for scaffold hopping
3. Include activity and property scoring functions
4. Generate diverse analogs maintaining key pharmacophores
5. Evaluate synthetic feasibility and drug-likeness

Fragment-to-lead optimization

1. Start with fragment hits from screening
2. Use fragment growing objectives in scoring
3. Include binding affinity predictions for target
4. Generate lead-like molecules from fragments
5. Assess ADMET properties of grown compounds

Multi-target optimization

1. Define multiple targets of interest
2. Create composite scoring function for selectivity
3. Include off-target activity penalties
4. Generate compounds optimized for target profile
5. Evaluate polypharmacology potential

Quality control and validation

Generation quality assessment

Molecular validity:

• All generated SMILES are chemically valid
• No unusual or reactive functional groups
• Appropriate molecular weight and complexity
• Drug-like properties within acceptable ranges

Scoring sanity:

• Property predictions align with expectations
• Your scoring model rates your known actives highly — if it does not, RL will optimize away from them
• Diversity metrics meet project requirements
• You are treating the output as design ideas for a chemist, not as candidates shown to be makeable or active (translatability)

Optimization validation

Training progress:

• Score improvement over training epochs
• Convergence to desired property ranges
• Diversity maintained throughout optimization
• No mode collapse or repetitive structures

Result diversity:

• Chemical space coverage appropriate for project
• Scaffold diversity vs. focused optimization balance
• Property distribution matches target profile
• Novelty assessment vs. training data

Integration with platform workflows

Upstream design inputs

Biological data:

• Target structures: Use DiffDock results for binding optimization
• Activity data: Incorporate screening results into scoring
• Property requirements: Define from ADMET profiling studies
• Synthetic constraints: Include feasibility from AiZynthFinder

Chemical data:

• Known ligands: Use scaffold hopping from active compounds
• Property filters: Apply from DeepSol solubility predictions
• SAR data: Incorporate structure-activity relationships

Downstream evaluation

Property assessment:

• ADMET profiling: evaluate with ADMET-AI predictions
• Solubility: use DeepSol for predicted aqueous solubility
• Synthetic planning: check accessibility with AiZynthFinder
• Target binding: propose a pose with DiffDock. Docking does not establish that a compound binds

Experimental validation:

• Compound synthesis: Plan routes with retrosynthesis tools
• Biological testing: Design assays based on predicted activity
• Analytical characterization: Predict properties for QC planning
• Formulation studies: Assess developability early

Performance optimization

Model selection and configuration

Model considerations:

• Choose appropriate model size for computational resources
• Select pre-trained models for faster convergence
• Consider fine-tuned models for specific chemical spaces
• Balance model complexity with generation speed

Training parameters:

• Set appropriate batch sizes for available memory
• Configure learning rates for stable optimization
• Adjust exploration parameters for diversity vs. convergence
• Monitor training stability and adjust as needed

Computational efficiency

Resource optimization:

• Utilize GPU acceleration when available
• Configure parallel scoring for faster evaluation
• Use appropriate batch sizes for memory efficiency
• Monitor computational costs vs. result quality

Scalability strategies:

• Start with smaller runs to validate approaches
• Scale up generation size based on initial results
• Use iterative refinement rather than single large runs
• Implement result filtering to manage output volume

Best practices for generative design

Project planning

Objective definition:

• Clearly define target property profiles and constraints
• Establish success criteria before starting generation
• Consider synthetic feasibility alongside optimization goals
• Plan for experimental validation of generated compounds

Data preparation:

• Curate high-quality training data when fine-tuning models
• Validate scoring functions against known active compounds
• Ensure property predictions are calibrated for your targets
• Consider chemical space coverage and diversity requirements

Generation strategy

Iterative optimization:

• Start with broad objectives and refine based on results
• Use intermediate evaluation to guide parameter adjustment
• Balance exploration of chemical space with focused optimization
• Consider multiple rounds of generation and selection

Quality assurance:

• Implement post-generation filtering and validation
• Assess synthetic accessibility of promising compounds
• Evaluate drug-likeness and developability early
• Cross-validate predictions with orthogonal methods

Result evaluation

Compound assessment:

• Evaluate generated compounds against project criteria
• Consider synthetic accessibility and cost of goods
• Assess intellectual property and patentability
• Plan experimental testing priorities

Success metrics:

• Measure achievement of target property profiles
• Assess diversity and novelty of generated compounds
• Evaluate hit rates in downstream screening
• Track progression toward lead optimization goals

Research reproducibility

Documentation:

• Record all generation parameters and scoring functions
• Preserve training data and model versions
• Document evaluation criteria and selection processes
• Generate comprehensive reports for project archives

Collaboration:

• Share generation protocols with team members
• Generate standardized compound evaluation reports
• Document methods for peer review and validation
• Maintain searchable compound libraries and results

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