Challenges
Drug discovery is slow, costly, and failure-prone
Diagnostic models often lack generalizability and interpretability
Scarcity of high-quality biomedical datasets
Long development cycles and expensive wet-lab validation
Generative AI for De Novo Drug Design
Challenge: Traditional drug design is slow and expensive, with many failures due to poor drug properties. AI can generate molecules that are both potent and drug-like.
Solution: AI-driven molecule generation designs novel compounds tailored for specific targets and optimizes both potency and drug properties simultaneously
Results: Increases the likelihood of clinical success, shortens development cycles and automates and accelerates the design-make-test cycle.
Generative AI for De Novo Drug Design
Challenge: Traditional drug design is slow and expensive, with many failures due to poor drug properties. AI can generate molecules that are both potent and drug-like.
Solution: AI-driven molecule generation designs novel compounds tailored for specific targets and optimizes both potency and drug properties simultaneously
Results: Increases the likelihood of clinical success, shortens development cycles and automates and accelerates the design-make-test cycle.
ML Solutions on precision medicine with Deep-ID
Challenge: Deep learning models in healthcare are often criticized as "black boxes," making it difficult to understand their decision-making process.
Solution: Multi-modal AI models integrate diverse biomedical data while maintaining interpretability, allowing clinicians to identify key biomarkers and disease patterns.
Results: Enhances diagnostic accuracy, enables early disease detection, and provides transparent, explainable AI-driven insights for precision medicine.
Facts: Top pharma company as a client
Our Solutions
DeNViS β AI-powered virtual screening
Rapid screening of over 1B molecules using GNNs, reducing time & cost
AI-driven de novo drug design
Generate & optimize molecules with improved ADMET properties
Deep-ID β Precision medicine
Multimodal AI models for disease diagnosis, prognosis & biomarker discovery
AI-guided treatment personalization
Leverage genomics, imaging, and clinical data to suggest optimal treatments
Results
Accelerated drug development timelines
Accelerated drug development timelines
Higher success rates in clinical trials
β
Faster & more accurate diagnoses
β
Personalized treatment plans
β Lower drug development costs