Our technology stack combines Graph Neural Networks, Digital Twin modeling, molecular docking, and high-performance computing to accelerate drug discovery from hypothesis to validation.
Foundational technology pillars powering every Living In Silico platform and research program.
Deep learning models trained on molecular graph representations combined with computational replicas of biological systems to predict antimicrobial activity at scale.
Physics-based and AI-enhanced docking simulations for predicting compound-target binding affinity and interaction profiles.
Built on cloud-native architecture with enterprise-grade security and scalable compute for large-scale molecular screening.
Scalable GPU-accelerated compute infrastructure for high-throughput molecular screening and deep learning model training.
Curated and proprietary compound libraries with rich chemical, biological, and pharmacological annotations for model training.
Enterprise security architecture protecting proprietary compound data, model weights, and research outputs across all platform interactions.
See how our technology translates into specialized AI platforms for antimicrobial drug discovery and sustainable biotechnology.