AI-Driven Drug Discovery

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.

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Research Programs

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.

01 — Oncology

Oncology Therapeutics

Targeting tumor microenvironments with AI-guided compound identification across solid tumors and hematologic malignancies.

02 — Genetics

Rare Genetic Disorders

Gene-level therapeutic design using deep learning models trained on human genome variant databases and protein folding datasets.

03 — Neurology

Neurodegenerative Diseases

Blood-brain barrier penetration modeling and neuroinflammation pathway analysis using our proprietary CNS compound database.

04 — Immunology

Immunotherapy Research

Adaptive immune response simulation and checkpoint inhibitor target discovery through large-scale clinical genomics analysis.

Platform Technology

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.

01

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.

02

Multi-Scale Molecular Simulation

GPU-accelerated molecular dynamics validate binding affinity predictions with quantum mechanical precision at industrial scale.

03

Predictive Compound Screening

Virtual screening across 800M+ compound libraries with ADMET property prediction and selectivity profiling in hours, not months.

04

Data-Driven Experimental Validation

Active learning loops integrate wet-lab assay data to continuously refine model predictions and prioritize experimental resources.

NexaGen Discovery Platform · v3.2.1
Compounds Screened
847M
current discovery cycle
Binding Accuracy
94.2%
affinity prediction
Binding Affinity Distribution — Active Cycle
NXG-0441 — Lead Candidate

Predicted IC₅₀: 2.3 nM · BRAF V600E · Selectivity 42:1

Confidence Score94%
Development Pipeline

From Discovery to the Clinic

Active Stage
Completed
Upcoming
NXG-0441
BRAF V600E · Melanoma
Oncology
Discovery
Preclinical
Phase I/II
Regulatory
NXG-0217
LRRK2 · Parkinson’s Disease
Neurology
Discovery
Preclinical
Phase I/II
Regulatory
NXG-0389
SMN1/SMN2 · Spinal Muscular Atrophy
Rare Disease
Discovery
Preclinical
Phase I/II
Regulatory
NXG-0502
PD-L1 · NSCLC Immunotherapy
Immunotherapy
Discovery
Preclinical
Phase I/II
Regulatory
Scientific Evidence

Peer-Reviewed Research
& Institutional Recognition

0
Peer-Reviewed Publications
0
Total Citations
0
Institutional Partners
0
Conference Keynotes
Nature Chemical Biology · 2024

Transformer Models Enable Accurate Prediction of Small Molecule Binding Affinities Across Diverse Protein Families

Chen, L.*, Patel, M.*, Reyes, A., Nakamura, T., Zhang, W., Osei-Bonsu, K. et al.

Citations: 318 Impact Factor: 12.6 DOI: 10.1038/s41589
Cell Systems · 2023

Multi-Scale Molecular Dynamics Simulation Guided by Deep Learning Reduces ADMET Failure Rates by 67%

Reyes, A., Hoffmann, K., Patel, M., Singh, R., Walker, J., Chen, L. et al.

Citations: 204 Impact Factor: 11.1 DOI: 10.1016/j.cels
JACS · 2024

Generative Molecular Design of Selective Kinase Inhibitors via Reinforcement Learning and Experimental Feedback

Nakamura, T., Walker, J., Chen, L., Osei-Bonsu, K., Hoffmann, K., Patel, M. et al.

Citations: 176 Impact Factor: 16.4 DOI: 10.1021/jacs
Partners & Investors

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.

Roche
Diagnostics
Novartis
Institutes
Pfizer
Research
Stanford
Medicine
MIT
CSAIL
Flagship
Pioneering
AstraZeneca
BioMedical
Harvard
Medical School
Sequoia
Capital Bio
Merck
KGaA
UCSF
Quantitative
a16z
Bio Fund
Leadership

Scientific & Executive Team

Dr. Lin Chen, Chief Executive Officer
Dr. Lin Chen
Chief Executive Officer

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.

MIT PhD Genentech 40+ Patents
Dr. Maya Patel, Chief Scientific Officer
Dr. Maya Patel
Chief Scientific Officer

Associate Professor, Stanford Chemical Engineering (on leave). DPhil, Oxford. Trained at the Broad Institute. Expert in protein structure prediction and ADMET modeling.

Oxford DPhil Broad Institute Stanford
Dr. Takeshi Nakamura, Chief Technology Officer
Dr. Takeshi Nakamura
Chief Technology Officer

Former Principal Scientist, Google DeepMind (AlphaFold team). PhD, Kyoto University. Architect of our core molecular transformer architecture and simulation infrastructure.

Kyoto PhD DeepMind AlphaFold
Dr. Amara Reyes, Chief Medical Officer
Dr. Amara Reyes
Chief Medical Officer

Board-certified oncologist and clinical researcher. Former Clinical Development VP at Roche Oncology. MD/PhD, UCSF. Leads all IND-enabling studies and clinical strategy.

UCSF MD/PhD Roche Oncology
Advancing Precision Medicine

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.

Contact Our Team Investor Relations