Cheminformatics & Reaction Design
AI-powered chemical design and synthesis planning using cutting-edge tools like RDKit, DeepChem, RXN for Chemistry, and Graph Neural Networks.
Request ConsultationAI-Driven Chemical Intelligence
Modern drug discovery and chemical synthesis require sophisticated computational tools to navigate vast chemical space, predict molecular properties, and design optimal synthetic routes.
Our Cheminformatics & Reaction Design services leverage state-of-the-art AI and machine learning to accelerate your chemistry programs, from hit identification to process optimization.
Our Cheminformatics Services
AI-Assisted Retrosynthesis
Plan efficient synthetic routes using AI-powered retrosynthesis tools and route optimization algorithms.
- Automated retrosynthetic analysis with RXN for Chemistry
- Multi-step synthesis planning
- Route scoring and optimization
- Commercial availability checking
Reaction Condition Prediction
Predict optimal reaction conditions including solvents, catalysts, temperature, and reagents using machine learning.
- Yield prediction models
- Solvent and catalyst selection
- Temperature and time optimization
- Reaction success probability scoring
Structure-Activity Relationship (SAR)
Build predictive models linking molecular structure to biological activity and physicochemical properties.
- QSAR/QSPR model development
- Molecular descriptor calculation
- Activity prediction for virtual screening
- Lead optimization guidance
Molecular Design & Optimization
Design and optimize molecules using graph neural networks and generative AI models.
- De novo molecule generation
- Property-driven optimization
- Drug-likeness assessment (Lipinski, etc.)
- ADMET property prediction
Technologies & Platforms
RDKit
Comprehensive cheminformatics toolkit for molecular manipulation and property calculation.
DeepChem
Deep learning library for drug discovery and molecular property prediction.
RXN for Chemistry
IBM's AI-powered platform for retrosynthetic planning and reaction prediction.
Graph Neural Networks
Advanced neural architectures for learning molecular representations and properties.
Applications
Drug Discovery
Identify and optimize lead compounds with desired biological activity and favorable drug-like properties.
Process Chemistry
Optimize synthetic routes for scalability, yield, cost-effectiveness, and environmental sustainability.
Material Science
Design novel materials with targeted properties for applications in polymers, catalysts, and functional materials.
Our Approach
Target Definition
Define chemical space, target properties, and success criteria.
Model Development
Build and train AI models on your proprietary and public data.
Virtual Screening
Screen virtual libraries and generate novel molecular designs.
Synthesis Planning
Design optimal synthetic routes with predicted yields and conditions.
Ready to Accelerate Your Chemistry Programs?
Contact us to discover how AI-powered cheminformatics can transform your drug discovery and chemical development.
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