SAVING LIVES,ACCELERATEDAI + Quantum Antibody Discovery
Regenova Pharmaceuticals develops monoclonal antibody therapeutics using AI-driven design and quantum-assisted molecular prediction — accelerating discovery from target identification to optimized candidates.
From Molecule to Medicine
A structured, computation-guided pathway — from initial candidate generation through to preclinical preparation and partnership readiness.
AI + Quantum Discovery
AI-guided models generate and prioritize monoclonal antibody candidates by evaluating sequence space and predicted binding profiles across target antigens.
Molecular Optimization
Structure-informed refinement supports binding, specificity, and developability assessment — iterating on variable domain architecture to narrow the candidate pool.
Wet Lab Validation
Selected candidates move toward experimental testing to confirm activity and biological relevance, bridging computational predictions with physical data.
Toxicity & Safety Prediction
Computational safety assessment helps identify potential development risks early — evaluating off-target effects and immunogenicity before advancing candidates.
Preclinical Planning
Promising candidates are prepared for preclinical validation and partnership discussions — defining study design, CMC considerations, and regulatory pathway.
Platform designed to support an accelerated candidate discovery workflow across multiple therapeutic areas.
VIEW OUR PIPELINEAccelerating Early
Antibody Discovery
Quantum Simulation
Quantum-assisted molecular modeling explores conformational space and atomic-level interactions to guide antibody candidate design.
AI Optimization
AI models evaluate and refine antibody sequences across large parameter spaces, prioritizing candidates based on predicted binding profiles.
Validation Planning
Computational predictions are used to design targeted experimental testing strategies, bridging in silico candidate selection with wet-lab confirmation.
Targeting Critical
Unmet Needs
Three active candidate programs in oncology and infectious disease, advancing through AI-guided discovery toward preclinical readiness.
Anti-HER2 Antibody
- AI-designed antibody candidate targeting HER2+ breast cancer
- Currently in candidate optimization and preparation for preclinical validation
Anti-Nipah Virus Antibody
- Early-stage antibody discovery program focused on Nipah virus targets
- AI-guided design and computational screening to identify candidates
Multi-target Antibody Platform
- Discovery platform supporting three active antibody programs and future expansion
- AI + quantum-assisted approach enabling scalable candidate generation
Why
Invest in
Regenova
Regenova combines novel computational methods, experienced leadership, and high-value therapeutic targets into a platform designed for long-term scalability and scientific differentiation.
Differentiated Technology
Integration of quantum simulation with AI optimization represents a novel computational approach to antibody candidate design — distinct from conventional in silico methods.
Platform Scalability
The discovery platform is designed to support multiple simultaneous therapeutic programs across disease areas, enabling portfolio expansion without linear cost increases.
High-Value Indications
Programs targeting HER2+ oncology and Nipah virus address large unmet medical needs with established commercial pathways and global health significance.
Experienced Team
Leadership with direct experience at Merck and AstraZeneca, combined with deep expertise in AI, quantum simulation, and clinical immunology.
Research & Program Focus
Active research areas and therapeutic programs advancing through AI-guided discovery and computational design.
Integrated Quantum-Classical Approach for Antibody Candidate Design
Research focus on combining quantum simulation with AI optimization to guide monoclonal antibody candidate selection — currently applied to the HER2 oncology program and Nipah virus infectious disease program.
VIEW PIPELINEAI-Guided Antibody Sequence Optimization
Research focus on using deep learning models to evaluate and prioritize antibody variable domain sequences based on predicted binding and developability profiles.
Quantum-Assisted Molecular Modeling
Exploring quantum simulation methods to model antibody-antigen interactions and conformational dynamics at atomic resolution.
HER2+ Breast Cancer Antibody Discovery
Late discovery program applying integrated computational design to identify and optimize antibody candidates targeting HER2.
Nipah Virus Antibody Discovery
Early discovery program focused on identifying antibody candidates against Nipah virus targets using AI-guided design.
Leadership & Scientific Advisors
Regenova is led by experienced professionals in clinical immunology, drug development, and AI-driven therapeutics, supported by expert advisors across biotech and pharmaceutical innovation.
Regenova Pharmaceuticals was founded to address one of the most critical bottlenecks in drug discovery — the time, cost, and complexity of identifying viable therapeutic candidates. Led by experts in clinical immunology, pharmaceutical development, and artificial intelligence, the team brings experience from leading organizations such as Merck and AstraZeneca. By integrating computational modeling with biological validation, Regenova is building a scalable platform for next-generation monoclonal antibody discovery.
Accelerate Your
Pipeline
Partner with Regenova to leverage quantum-AI antibody discovery for your therapeutic programs. Let's discuss collaboration opportunities.
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