Aetheris Bio pioneers continuous 3D equivariant diffusion transformers and high-throughput tensor-accelerated physics engines. We engineer de novo macro-therapeutics and resolve previously undruggable disease targets in minutes with sub-angstrom conformational precision.
Experience our continuous SE(3) diffusion sampler live. Select high-affinity disease targets, trigger tensor-accelerated conformational refinement, and observe atomic free-energy convergence in real time.
Cryo-EM constrained docking & thermodynamic validation
Traditional drug discovery is stalled by brute-force Newtonian molecular dynamics and flat 2D chemical language models. Aetheris Bio solves this through a four-pillar proprietary computing stack.
Our 14B parameter foundation model operates directly on continuous 3D Riemannian manifolds with native $SE(3)$-equivariance. Rotations and translations do not alter energy outputs.
Directly ingests 3D electron density volumes from cryo-electron microscopy. The neural engine performs all-atom rotamer refinement and water displacement thermodynamics in milliseconds.
Custom low-level warp reduction algorithms fused into FP8 execution kernels. Delivers ultra-low latency inference across distributed enterprise clusters connected by 900 GB/s high-speed fabrics.
Our computational predictions are connected directly to automated micro-fluidic synthesis stations. In vitro binding kinetics feed back into our loss functions weekly to prevent distribution shift.
Just as large language models scale with token compute, our $SE(3)$ diffusion models demonstrate empirical power-law improvements in ligand affinity prediction ($R^2 = 0.94$) as parameter density and cluster FLOPs increase.
92.4% scaling efficiency across 512 distributed nodes utilizing 900 GB/s high-bandwidth interconnects.
SmoothQuant activation clipping reduces memory footprint by 52% with zero degradation in binding free energy accuracy.
Pretrained across 1.4 billion simulated all-atom conformational ensembles before target-specific fine-tuning.
Adjust model parameter scale to project compute requirements
Aetheris Bio couples proprietary generative architectures with aggressive in-house asset progression across high-unmet-need oncological and neurodegenerative targets.
| Program | Target & Indication | Modality | Target Discovery | Lead Optimization | In Vitro Validation | Commercial Rights |
|---|---|---|---|---|---|---|
| AB-101 |
KRAS-G12D (Switch-II)
Pancreatic Ductal & Colorectal Adenocarcinoma
|
Macrocyclic Peptidomimetic | Complete | Complete | Active (Kd 0.84nM) | 100% Aetheris Bio |
| AB-204 |
HER2 Subdomain IV (De Novo)
Refractory Breast & Gastric Cancers
|
Engineered Synthetic CDR-H3 | Complete | In Progress | — | 100% Aetheris Bio |
| AB-318 |
Tau Paired Fibril Aggregate
Alzheimer's Disease & FTD Tauopathies
|
Brain-Penetrant Small Molecule | Complete | Sampling Manifold | — | 100% Aetheris Bio |
| AB-409 |
Voltage-Gated Nav1.7 Channel
Severe Neuropathic Pain & Erythromelalgia
|
Subtype-Selective Modulator | Target Cryo Mapping | — | — | Available for Co-Dev |
Our interdisciplinary leadership unites world-class experts in distributed tensor acceleration, structural biology, and translational medicine.
Tanaji Vishnu Yadav is a veteran computational scientist and deep-tech entrepreneur specializing in high-performance hardware orchestration, $SE(3)$ geometric deep learning, and continuous molecular dynamics manifolds. Prior to founding Aetheris Bio, he led research initiatives engineering distributed tensor reduction engines and high-throughput physical simulations.
Author of 7+ peer-reviewed publications across top-tier venues (NeurIPS, CVPR, and Computational Biophysics) with over 1,800 citations, and named inventor on 2 core provisional patents in high-speed molecular generative diffusion. Under his leadership, Aetheris Bio has achieved single-minded technical velocity, developing proprietary FP8 execution kernels that accelerate atomic simulation by over 1,200x.
Head of Structural Biology
Former Cryo-EM Research Group Leader at Max Planck Institute of Biochemistry. Over 12 years of structural elucidation experience analyzing multi-protein oncogenic complexes and membrane receptors.
VP of Distributed Compute
Former High-Performance Systems Fellow at CERN & EPFL. Specializes in warp-level cooperative reduction algorithms, FP8 tensor quantizations, and petascale low-latency cluster scheduling.
Chief Medical Officer
Former Translational Oncology Fellow at Dana-Farber Cancer Institute & Harvard Medical School. Leads pre-clinical IND-enabling studies, pharmacokinetic profiling, and clinical candidate progression.
Guided by pioneers in computational chemistry, macrocyclic pharmacology, and enterprise supercomputing infrastructure.
Access the Aetheris Core engine directly via our high-throughput Python SDK or REST endpoints. Integrate de novo molecular sampling and automated scoring into your enterprise screening workflows.
# Install client: pip install aetheris-bio
import aetheris as ab
# Initialize the 14B SE(3) Equivariant Model Engine
client = ab.Client(api_key="aeth_live_89f0293")
# Generate de novo macro-cyclic binder for Oncogene Target
candidate = client.diffuse(
target_pdb="7LUD",
binding_site={"center": [24.1, -12.5, 18.2], "radius_angstrom": 8.0},
scaffold_type="macrocyclic_peptide",
target_kd_nanomolar=1.0,
conformations=50
)
# Output high-confidence docked candidate
print(f"Top Candidate SMILES: {candidate.smiles}")
print(f"Predicted Binding Energy: {candidate.delta_g} kcal/mol")
print(f"RMSD vs Target Pocket: {candidate.rmsd} Å")Explore our 12-slide comprehensive presentation covering commercial validation, defensible $SE(3)$ IP, $180B market opportunity, petascale cluster scaling laws, and financial roadmaps.
Tanaji Vishnu Yadav, Founder & CEO
Everything you need to know about our architectural defensibility, compute fabric, and partner engagements.
Collaborate with our computational biology team to screen high-unmet-need targets or license our proprietary oncology assets. Inquiries reviewed directly by executive leadership.