AETHERIS BIO
Generative Atomic Foundation Models for Petascale Biomolecular Engineering
Harnessing continuous $SE(3)$-equivariant diffusion transformers and low-latency tensor hardware acceleration to design de novo macro-therapeutics for undruggable targets.
Why 90% of High-Value Disease Targets Remain Undruggable
Crippling Simulation Latency
Classical all-atom molecular dynamics (e.g. AMBER, GROMACS) requires 2 to 4 months of cluster time to sample a single nanosecond protein-ligand conformational trajectory.
Coordinate Hallucinations
2D chemical language models (SMILES transformers) lack 3D physical spatial geometry. They generate molecules with severe steric clashes, unnatural bond angles, and zero binding affinity.
$2.6B & 12+ Year Discovery
Pharma spends an average of $2.6 Billion per approved drug, with an 88% attrition rate in early clinical trials due to off-target toxicity and inaccurate binding predictions.
A Continuous 3D Equivariant Foundation Model for Biology
Aetheris Bio has developed Aetheris Core™, a 14.2B parameter multimodal foundation model that bridges atomic physics and generative $SE(3)$ diffusion.
Architectural & Mathematical Moat
Continuous Manifold
Continuous Riemannian representations eliminate discretization artifacts in high-dimensional atomic coordinate space.
Warp-Fused Kernels
Custom low-level tensor reductions written directly for FP8 high-bandwidth clusters, eliminating PyTorch overhead.
1.4B Pretraining Ensembles
Proprietary synthetic physics dataset representing non-equilibrium protein-ligand unbinding pathways.
2 Core Patents
Provisional patent filings covering equivariant diffusion for macrocyclic peptidomimetics and all-atom docking.
High-Value Internal Therapeutic Pipeline
Triple-Engine Monetization Strategy
Asset Out-Licensing
Advancing internally generated lead compounds (e.g. AB-101) through Phase 1a, then licensing to global pharma for $50M-$150M upfront plus commercial sales royalties.
Pharma Co-Development
Joint discovery campaigns on high-priority partner targets. Includes \$5M-\$15M research upfronts, clinical milestones, and downstream milestone sharing.
Enterprise Compute SaaS
Tiered cloud access for discovery chemistry departments to run high-throughput virtual screening, charged on a consumption basis per thousand conformations sampled.
A $180 Billion Structural Transformation
Why Accelerated Compute is the Fundamental Bottleneck
To train and scale our 14.2B parameter model to a 48.5B Multimodal Frontier Model, our architecture requires massive parallel GPU tensor hardware:
- 41,600 Cluster Training Hours: Distributed across 8x high-bandwidth accelerated nodes connected via 900 GB/s fabrics.
- FP8 Warp-Level Execution: Custom reduction kernels authored directly for low-latency FP8 tensor execution with zero accuracy loss.
- Cryo-EM Voxel Grid Processing: Ingestion of terabyte-scale cryo-EM tomograms requires high-bandwidth unified memory buffers.
Clear Technical & Commercial Differentiation
| Feature / Metric | Aetheris Bio | AlphaFold 3 | Schrodinger Suite | Traditional CRO |
|---|---|---|---|---|
| De Novo Generation | Sub-Angstrom | Static Folding Only | Virtual Screening Only | Slow Wet-Lab Only |
| SE(3) Equivariance | Native Manifold | Partial Invariance | None (Cartesian) | N/A |
| Time per Conformation | 0.78 Seconds | 30 - 90 Seconds | 12 - 48 Hours | 3 - 6 Months |
| Native Cryo-EM Density | Direct Voxel Grid | Sequence Only | Manual Rigid Docking | Manual Modeling |
| IP Ownership Model | 100% Partner Retained | Academic / Shared | Enterprise License | Fee for Service |
24-Month Execution Roadmap
Foundation Scaling
- 14.2B parameter model validation
- AB-101 in vitro binding assays
- Seed accelerator cohort closing
48B Frontier Launch
- Scale to 48.5B parameter model
- First Top-5 pharma co-dev pilot
- Provisional patents conversion
IND-Enabling Studies
- AB-101 animal pharmacokinetic GLP
- Automated synthesis robot station
- Series A expansion ($15M)
Clinical Phase 1
- First-in-human trial filing
- Global pharma out-licensing option
- $40M+ upfront milestone target
World-Class Technical & Scientific Leadership
Tanaji Vishnu Yadav
Founder, CEO & Chief Scientist
Veteran deep-tech architect specializing in distributed high-performance tensor systems, geometric deep learning, and continuous $SE(3)$ molecular manifolds. Author of 7+ foundational papers across NeurIPS and Biophysics with 1,800+ citations, and holder of 2 provisional patents in molecular diffusion.
Join the Frontier of Atomic AI
We are closing our $3.5 Million Seed Round alongside applying for $200,000+ in Enterprise Accelerated Compute Credits to scale our 14B model to 48B parameters.