AtomNet
Deep-learning virtual screening to find novel small-molecule hits faster
Executive Summary
AtomNet is Atomwise’s (now Numerion Labs) structure-based AI platform that predicts protein–small-molecule binding to accelerate early drug discovery. It is used by pharma/biotech R&D teams and academic labs to run virtual high-throughput screening (vHTS) across extremely large, synthesizable chemical libraries and prioritize compounds for synthesis and experimental testing. The business value is faster hit discovery with higher hit rates and more structurally novel scaffolds than traditional screening alone, reducing wet-lab cost and time-to-hit. Differentiation includes deep learning built for 3D protein–ligand interactions and the ability to work even when little/no target-specific ligand data exists, enabling work on challenging/“undruggable” targets.
Use Cases
- Virtual high-throughput screening to identify initial hits for a protein target
- Finding novel scaffolds for targets with limited or no known ligands (hard-to-drug targets)
- Prioritizing compounds for synthesis-on-demand procurement and experimental validation
- Supporting partner programs from hit identification through lead optimization and candidate selection
- Running multi-target discovery programs within strategic pharma collaborations
Features
Visibility
- Virtual screening campaign outcomes: Summarizes results of vHTS runs (hit identification success across targets; campaign-level outputs used to drive next experimental steps).
- Structurally novel hit identification: Emphasizes discovery of new scaffolds and structurally distinct bioactive compounds per target, supporting novelty-driven R&D.
- Target-class breadth reporting: Demonstrated applicability across broad protein classes and many targets, with published multi-target study outcomes used as platform validation.
Intelligence
- Structure-based deep learning binding prediction: Deep convolutional network models 3D protein–ligand interactions to predict bioactivity in structure-based discovery.
- Low-data target support: Designed to be applied even when there are no known active ligands for a target or close homologs, enabling work on challenging biology.
- vHTS to narrow chemical space: Reduces millions/billions of candidate molecules to manageable sets of predicted hits for procurement/synthesis and assay testing.
- Hit-to-lead collaboration workflow: Used in partner collaborations spanning hit identification through lead optimization and candidate selection (delivered as a service/platform partnership model).
Support
- Partnered scientific collaboration model: Atomwise/Numerion Labs teams collaborate with partners through stages from hit finding to optimization and selection.
- Academic partnering programs (AIMS legacy): The platform has been used broadly with academic labs via programs like AIMS, enabling access to AI screening resources and joint research outputs.
Technical Specifications
- Architecture
- AI/ML drug-discovery platform for structure-based virtual screening (implementation details not publicly disclosed)
- Deployment
- Cloud/SaaS
- Authentication
- Not publicly disclosed
- API Available
- No
- MCP Server
- No
AI/ML Stack
- Machine Learning
Integrations
- Molecular Databases
- Laboratory Systems
Security & Compliance
Encryption: Not publicly disclosed
Pricing
- Model
- Custom enterprise / research collaboration (platform + services)
- Starting Price
- Contact sales
- Target Customer
- Mid-market to Enterprise (biopharma/biotech) plus academic/research institutes via partnerships
- Contract Type
- Typically multi-month/annual and/or milestone-based collaborations (terms vary by partnership)
- Free Trial
- No (credit card required)
About Atomwise
Atomwise is a biotechnology company that leverages artificial intelligence (AI) and machine learning to accelerate drug discovery and development. They invented the use of deep learning for structure-based drug discovery and are developing a pipeline of small-molecule drug candidates advancing into preclinical studies.