Pioneering generative AI for pharmaceutical and material innovation with extensive wet lab validation.

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10B
Virtual Screening / Day
100+
Drug Design Projects
20+
PCT Patents
1B
Parameter Foundation Model
80%
Cost Reduction
10-60%
Wet Lab Hit Rate

About Us

Most Validated AI Therapeutic Design Platform

Ainnocence is a global biotech company that leads in generative AI for therapeutic discovery, leveraging industry-grade sequence-based proprietary technology and training data from over 16 years of expertise.

The Challenge

Costly high-throughput wet lab screening, expensive structure biology, and inaccurate 3D modeling processes.

Our Solution

A radical AI drug discovery system accelerating life-saving therapies with unprecedented speed, deep virtual throughput, and higher success rates.

Featured White Paper

Validated by the American Chemical Society

Destructured Drug Discovery — ACS / C&EN White Paper featuring Ainnocence
View PDF →
ACS / C&EN White Paper • 2025

Destructured Drug Discovery

How sequence-based AI speeds and expands the search for new therapeutics — a third-party white paper from the American Chemical Society's C&EN division featuring Ainnocence's platform and founder Lurong Pan.

  • ▸ Published by the American Chemical Society's C&EN division
  • ▸ 80% reduction in early drug discovery cost
  • ▸ Spearman ρ = 0.441 — matches structure-based models at lower compute
  • ▸ Validated on SARS-CoV-2 broad-neutralizing antibodies
Download White Paper View Online

Platform Evolution

Platform Lineage

From Communication Dynamics Theory (2015) to Foundation Models (2026)

2015
THEORETICAL FOUNDATION
Communication Dynamics Framework

Communication Dynamics (CD)

Reality as Information — Physical & biological systems modeled as Shannon communication channels

Key Publications

📄 Paper I: Pan, Güldal, Baugh & Tanik, SDPS 2015
"Communication Theory Based Mathematical Models for Biomolecular Systems"

📄 Paper II: Pan, Skidmore, Güldal & Tanik, JIDPS 2022
"Application to Modelling the Valence Shell Orbitals of Periodic Table Elements"

Dimensional Reduction

10,000× computational efficiency vs conventional QM/MD models

2021
PLATFORM DEVELOPMENT
10 Modality-Specific Models Built on CD Framework
📊 GRAPH REPRESENTATION
Topology • 3D geometry • Interaction networks • Structure-aware molecular modeling
CarbonAI®
Small Molecule Design
Lead generation, scaffold hopping, PROTAC design. Target selectivity & ADME optimization.
No structure required • Wet-lab validated
MaterialAI™
New Material Design
High-performance alloys, functional materials, composites, nanomaterials.
Crystal structure prediction • Process optimization
NatmolAI®
Natural Molecule AI
800K+ natural molecule database screening. Traditional medicine modernization.
Source tracking • ADME enhancement
FormulaAI™
Chemical Formula Design
Formula performance prediction, process route optimization.
Knowledge graph driven • Cost prediction
CosmeticAI™
Cosmetic Innovation
Small molecule & peptide design for functional skincare.
Anti-aging • Hair care • Dermatological treatments
SynMagic™
Retrosynthesis
Multi-step synthesis planning, reaction prediction.
10M+ known reactions database
🧬 SEQUENCE REPRESENTATION
Linear order • Residue tokens • Sequence PLM, structure-free peptides
SentinusAI®
FLAGSHIP
Protein Design Engine
De novo protein design (IgG, Fab, scFv, VHH, peptides). Affinity maturation, humanization.
De Novo Design
50%
Success rate without structure info
Affinity Maturation
85.7%
Success rate (42 projects)
Efficiency
10,000x
Cost reduction vs 3D modeling
No structure required
•
Wet-lab validated since 2021
•
Picomolar potencies
CellulaAI™
Cell Programming AI
Intelligent cell engineering, AI-optimized cell line development.
Comprehensive genome screening • Real-time analytics
BioSynthAI™
Synthetic Biology Engine
Gene design optimization, strain screening & evolution.
Green biopharmaceuticals • Industrial enzymes
SenseAI™
RNA Design Engine
Therapeutic RNA design & modification. mRNA codon optimization, siRNA design.
mRNA vaccines • siRNA drugs • Protein replacement
PeptideAI®
Peptide Design Engine
Cyclic peptides, stapled peptides, bioactive peptide identification.
Drug development • Cosmetics • Vaccine design
2026
FOUNDATION MODELS
Unified Multi-Modal Architecture
CHEMICAL

Molecular graphs • Reactions • Physicochemical space

Unifies: CarbonAI, MaterialAI, NatmolAI, FormulaAI, CosmeticAI, SynMagic
Training Data
200M chemicals
Parameters
Up to 1B
Architecture
Graph Neural Net
DNA / PROTEIN

AINN-P1 • Genomic • Protein • Peptide • RNA sequence space

Unifies: SentinusAI, PeptideAI, CellulaAI, BioSynthAI, SenseAI
Training Data
53M sequences
Parameters
Up to 1B
Architecture
Transformer
⟷
CHEMICAL ⟷ DNA/PROTEIN
Bidirectional cross-modal generation through shared latent space
Molecule → protein interaction | DNA sequence → compound design

Foundation Model Architecture

CD-Transformer Architecture
  • • Block-circulant CDLinear layers: B× parameter reduction
  • • FFT-diagonalized Hessian: 310× better conditioning
Self-developed LLM Foundation
  • • Mixture-of-Experts (MoE) architecture
  • • Multi-head Latent Attention (MLA)
  • • Multi-Token Prediction (MTP) heads
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Research

Scientific Publications

Peer-reviewed research spanning AI, drug discovery, and computational biology

ACS / C&EN White Paper — Destructured Drug Discovery
FEATURED ACS / C&EN White Paper • 2025

Destructured Drug Discovery: How Sequence-Based AI Speeds and Expands the Search for New Therapeutics

An American Chemical Society / C&EN white paper featuring Ainnocence's sequence-based AI platform — bypassing 3D structural modeling to screen billions of drug candidates in hours and cut early-discovery cost by 80%.

Download PDF →
Nature Scientific Reports • 2025

AI-designed, mutation resistant broad neutralizing antibodies against multiple SARS-CoV-2 strains

Demonstrating the power of sequence-based AI for therapeutic antibody design with unprecedented speed and accuracy.
SDPS 2015 • JIDPS 2022 • arXiv 2026

Communication Dynamics Theory Series

Three foundational papers spanning biomolecular systems, periodic table orbitals, and neural network architecture

Pan et al. — Mathematical foundation for all Ainnocence platforms

Multiple Publications

AI Drug Discovery & Virtual Screening

CarbonAI platform, ParaVS framework, antibody affinity maturation, and more

14+ additional peer-reviewed publications

View All 17 Publications

Intellectual Property

Patent Portfolio

20+ PCT patents protecting our AI drug discovery methodologies

20+
PCT Patents Filed
10+
Platform Technologies
Global
Coverage
View Patent Portfolio

Contact

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