Engineering the Future of Therapeutics with Artificial Intelligence
Our platform accelerates drug discovery by modeling molecular interactions at unprecedented scale — transforming biological complexity into actionable therapeutic targets.
Precision Targeting
Across Key Indications
We apply transformer-based machine learning models trained on multi-omics datasets to identify high-confidence therapeutic targets across four critical disease categories, each selected for unmet medical need and AI tractability.
Oncology Therapeutics
Targeting tumor microenvironments with AI-guided compound identification across solid tumors and hematologic malignancies.
Rare Genetic Disorders
Gene-level therapeutic design using deep learning models trained on human genome variant databases and protein folding datasets.
Neurodegenerative Diseases
Blood-brain barrier penetration modeling and neuroinflammation pathway analysis using our proprietary CNS compound database.
Immunotherapy Research
Adaptive immune response simulation and checkpoint inhibitor target discovery through large-scale clinical genomics analysis.
AI-Powered
Molecular Intelligence
Our integrated computational platform combines transformer-based molecular language models with physics-based simulations to reduce early-stage discovery timelines by an order of magnitude.
Transformer Molecular Models
Pre-trained on 2.4 billion molecular structures, our foundational model encodes chemical language across SMILES representations, protein sequences, and binding site geometries.
Multi-Scale Molecular Simulation
GPU-accelerated molecular dynamics validate binding affinity predictions with quantum mechanical precision at industrial scale.
Predictive Compound Screening
Virtual screening across 800M+ compound libraries with ADMET property prediction and selectivity profiling in hours, not months.
Data-Driven Experimental Validation
Active learning loops integrate wet-lab assay data to continuously refine model predictions and prioritize experimental resources.
NXG-0441 — Lead Candidate
Predicted IC₅₀: 2.3 nM · BRAF V600E · Selectivity 42:1
From Discovery to the Clinic
Peer-Reviewed Research
& Institutional Recognition
Transformer Models Enable Accurate Prediction of Small Molecule Binding Affinities Across Diverse Protein Families
Multi-Scale Molecular Dynamics Simulation Guided by Deep Learning Reduces ADMET Failure Rates by 67%
Generative Molecular Design of Selective Kinase Inhibitors via Reinforcement Learning and Experimental Feedback
Institutional
Collaboration Network
Strategic partnerships with world-leading pharmaceutical companies, academic medical centers, and life science investors who share our commitment to evidence-based therapeutic development.
Diagnostics
Institutes
Research
Medicine
CSAIL
Pioneering
BioMedical
Medical School
Capital Bio
KGaA
Quantitative
Bio Fund
Scientific & Executive Team
Former Director of Computational Chemistry at Genentech. PhD, MIT Department of Chemistry. Pioneer in ML-guided molecular design with 40+ patents in AI-driven therapeutic discovery.
Associate Professor, Stanford Chemical Engineering (on leave). DPhil, Oxford. Trained at the Broad Institute. Expert in protein structure prediction and ADMET modeling.
Former Principal Scientist, Google DeepMind (AlphaFold team). PhD, Kyoto University. Architect of our core molecular transformer architecture and simulation infrastructure.
Board-certified oncologist and clinical researcher. Former Clinical Development VP at Roche Oncology. MD/PhD, UCSF. Leads all IND-enabling studies and clinical strategy.
The Future of
Drug Discovery
is Computational
We are seeking strategic pharmaceutical partnerships and Series B investment to advance our clinical pipeline. Engage our leadership team to explore collaboration opportunities aligned with your therapeutic focus areas.