Seeding Thoughts
Graph Neural Networks (GNNs)
Predict drug-target interactions, optimize lead selection, and identify repurposing opportunities.
Generative AI & Variational Autoencoders (VAEs)
Generate novel molecular structures tailored for specific biological targets. AI-driven de novo molecule design, compound refinement, and safety optimization.
Digital Twins for Personalized Medicine
AI-driven simulations model patient-specific responses to drug compounds - for patient response simulations and biomarker-driven precision medicine.
Reinforcement Learning (RL) for Drug Optimization
AI continuously refines molecular properties based on efficacy and safety predictions.
Patient to Trial Matching
Patient recruitment is one of the biggest hurdles in clinical trials. ScienOps' AI solutions ensure precision-driven, diverse patient matching and faster enrollments.
AI for Pharmacovigilance & Safety Monitoring
Predicts adverse events, drug interactions, and real-world safety risks.
High-Throughput AI Screening (HTS)
AI analyzes millions of compounds, ranking the most promising candidates.
Graph Neural Networks (GNNs) & AlphaFold
Predict drug-target interactions, optimize molecular design, and accelerate lead discovery.
Bayesian adaptive trials
Predictive modeling reduces protocol deviations and enhances adherence.
Retrieval Augmented Generation (RAG)
RAG & RPA for Regulatory Compliance – AI-driven automation for document processing, adverse event detection, and compliance tracking.
Robotic Process Automation (RPA) and Agentic Workflows
Synthetic Control Arms
Real-world data replaces placebo groups, reducing patient burden.