Computational Methods for Peptide Discovery
Axia Discovery's platform combines classical computational chemistry with modern deep learning to accelerate in-silico peptide discovery and optimization.
Deep Learning Models
Proprietary neural networks trained on structural data, binding kinetics, and ADMET properties. Continuously refined with experimental validation.
Molecular Docking
Accurate physics-based simulations to predict peptide-target binding modes and calculate affinity scores across virtual compound libraries.
De Novo Design
Generative AI methods that design novel peptide sequences optimized for target engagement while maintaining drug-like properties.
ADMET Prediction
Machine learning models for absorption, distribution, metabolism, excretion, and toxicity profiling to identify promising lead candidates.
Peptide Chemistry
Specialized methods for peptide modification, cyclization, and stabilization to improve bioavailability and metabolic stability.
Experimental Validation
Closed-loop feedback between computational predictions and wet lab validation to improve model accuracy and pipeline efficiency.
Why Peptides?
↑ Target Specificity
Peptides offer exquisite selectivity for challenging targets like protein-protein interactions and conformational epitopes that small molecules struggle to address.
↑ Chemical Space
The peptide chemical space is vast and largely unexplored. Rational design enables rapid optimization of potency, selectivity, and ADMET properties.
↑ Scalability
Computational design accelerates peptide discovery 10–100× compared to traditional approaches, enabling faster path to IND and clinical development.
Technology Foundation
Machine Learning & AI
Proprietary deep learning models built on PyTorch and JAX, trained on curated peptide-target interaction datasets and refined continuously via experimental feedback.
Frameworks: PyTorch · JAX · TensorFlow · Graph Neural Networks
Computational Chemistry
Structure-based and ligand-based design workflows, molecular dynamics, free energy calculations, and physics-aware scoring functions.
Tools: AutoDock · Rosetta · Gromacs · AMBER · Custom Scoring
Data Integration
Multi-modal data pipeline combining target genetics, expression patterns, chemical libraries, ADMET databases, and proprietary experimental results.
Sources: PubChem · ChEMBL · UniProt · TCGA · In-House Data
Faster hit identification vs. traditional screening
Binding affinity prediction accuracy on validation sets
Virtual compounds screened in silico
Want to learn more about our technology?
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