SynX Technology Platform

AI-Powered Micro/Nano Delivery Platform — GreenMatrix · OpenGMater · Ultra-High-Throughput Microfluidics · SynShell Continuous-Flow Manufacturing
100+
Green Material Systems
30+
AI Predictive Models
20+
Granted Patents
5
Hospital Validations
SynX:AI-Powered Micro/Nano Delivery Platform GreenMatrix™ Green Material Library 100+ Material Systems Monomers · Lipids · Polymers natural wall materials OpenGMater AI FoundationModel Data → Model → Agent Million-scale formulation design space Design · Execute · Learn Microfluidics Ultra-High-Throughput Microfluidics 5 Functional ModulesAutomation μL -Level RapidScreen Dry-Wet Experiment Flywheel SynShell™ Continuous-Flow Mfg 4 Major Process Systems Parallel Scale-Up In-line Detection · Closed Loop Target Markets Pharma · MediShell™ Cosmetics · AromaShell™ Agro · AgriCore™ Nutra · NutriCaps™ Full-stack delivery:formulation → targeting → live biotherapeutics AI Agent Execution → Data returns → Model iteration → Next experiment recommendation Materials AI Screen Mfg
GreenMatrix · Material Library
100+ Green Material Systems, Covering monomers/crosslinkers、lipids/lipidoids、polymers/ three core domains, for AI Model Provides ~100K CandidatesMaterials training foundation.
OpenGMater · AI Engine
Delivery Foundation Model, Encodes formulation-process-property-application into a unified "delivery language", Multi-Agent Parallel Reasoning, Driving intelligent design of liposomes/NPs/microcapsules.
Microfluidics + SynShell · Closed Loop
Ultra-High-Throughput Microfluidics μL -Level RapidScreen → AI Real-Time Learning Feedback;SynShell Parallel Scale-UpSeamlessly translate lab resultsforcontinuous flowProduction.
100+
Green Material Systems
~100K
Candidates Encoded
3
Core Material Domains
Monomers · Lipids · Polymers
20+ Years
Cumulative R&D Experience
Monomers / Crosslinkers Monomers / Crosslinkers PEGDA · GelMA · HEMA Polysaccharides · Protein Crosslinking pH/Enzyme/ROS Responsive Groups Microsphere Shell · Hydrogel Matrix Lipids / Lipidoids Lipids / Lipidoids Phospholipids · Sterol Lipids · Cationic Lipids PEGylated Lipids · Targeting Ligands Ionizable Lipids · Lipidoid Polymers LNPs · Liposomes · Nanovesicles Polymers / Naturals Polymers / Naturals γ-PGA · Hyaluronic Acid · Alginate Chitosan · Gelatin · Cellulose PLGA · PCL · Block Copolymers Microcapsules · Microspheres · Microgels Unified Encoding · Machine-Readable Tokens LIP[T:C18|PEG:2k] · PRC[FRR3.0|pH4.0] · QBD[D82|P12] · APP[Liver|mRNA] · RSP[EE91|Stb+]
Differentiation: Unlike traditional trial-and-error material development, GreenMatrix encodes complete material-process-property-application data into unified tokens, enabling AI models to systematically search and optimize across millions of formulation possibilities — dramatically compressing the cycle from material discovery to formulation validation.
DATA INGESTION GreenMatrix Library ~100K CandidatesMaterials · Formulation · Literature ProcessParameters · Characterization · Application Data Tokenization LIP · PRC · QBD · APP · RSP Structure · Process · Property Foundation Model Transformer / LLM Architecture Pre-training · Fine-tuning · RAG · RL Joint Representation:"Formulation-Process-Properties-Application" Multi-Agent Execution AI Orchestration · Parallel Reasoning Experiment Planning · Auto Dispensing Formulation Generation Input:Payload/Target/Dosage Form Goals Output:CandidatesFormulation + ProcessParameters One-Line Requirement → Recipe Property Prediction Particle Size / PDI / EE Prediction Release Kinetics · Stability Assessment 30+ AI Predictive Models Process Recommendation Flow Rate · Solvent Ratio · pH Ionic Strength · Crosslinking Temperature · ConcentrationOptimization Experiment Decision AI Orchestrator Decision Next Experiment Recommendation Learn on the Fly Auto-Feedback → Next Cycle:Design · Execute · Learn Closed Loop Data Collection → Token Encode → ModelPre-training/Fine-Tuning → Multi-Agent Parallel Reasoning → Experiment Execution → Data Return → ModelIteration ── Perpetual Flywheel Tens of thousands of formulations / literature / experimental data → Machine-readable tokens → Autonomous experimentation
"Speed is the ultimate weapon": OpenGMater transforms formulation design from empirical trial-and-error to model-driven discovery. Input payload, target, and dosage form requirements → the model generates candidate formulations → agents auto-execute dispensing and characterization → data returns in real time → the model learns and recommends the next experiment. This "dry-wet experiment flywheel" achieves order-of-magnitude efficiency gains in delivery system R&D.
5 FUNCTIONAL MODULES Module 1 Droplet Generation T-junction · Flow-focus Module 2 In-line Mixing/Reaction Rapid Mixing · React Module 3 In-line Detection Particle Size · PDI Module 4 Curing/Collection UV · Thermal · Chemical Module 5 Vision Guidance Vision Recognition HIGH-THROUGHPUT SCREENING PARAMETERS Material Ratio Flow Rate Solvent Ratio Concentration pH Ionic Strength Crosslinking Temperature μL -Level Sample Consumption → RapidScreen → Real-Time Feedback AI Platform → AI Iterative Optimization → Next Wet Experiment
μL
-Level Sample Consumption
Ultra-Low Material Cost
5
Functional Modules
Full Automation
Real-Time
In-line Detection
Size · PDI · Encapsulation
4
Formulation Systems
LNP · NP · MC · MS
Vision-Guided Droplet Manipulation: A proprietary vision recognition model enables precise micro-droplet manipulation and high-throughput screening, with AI-based real-time analysis of particle size distribution and morphology — automatically flagging optimal formulations and feeding back to OpenGMater for the next design iteration. The dry-wet experiment cycle compresses from "weeks per round" to "multiple rounds per day."
TRADITIONAL vs SynShell APPROACH Traditional: Scale-Up Reactor 1L → 10L → 100L reactor stepwise scale-up ✗ Size drift · Encapsulation drop · Batch variation SynShell: Parallel Scale-Up Micro-scale mixing units × N in parallel ✓ Consistent size · Stable EE · Reproducible batches 4 MAJOR PROCESS SYSTEMS LNP Process System Continuous LNP Preparation Polymer NP Process System PLGA · PCL · Block Copolymers Microcapsule Process System γ-PGA · Alginate · Chitosan Microsphere Process System HMP · Uniform Microspheres Continuous Feed → In-line Mix → In-line Detection(Particle Size/PDI/EE)→ Continuous Collection → QC Release Core Advantage: Parallel Scale-Up · Continuous Feeding · In-Line Detection · Micro-Scale Mixing Fidelity
Traditional Scale-Up Pain Points
· Scale-up reactors → Uneven mixing
· Widening PSD, increasing PDI
· Reduced encapsulation efficiency
· Uncontrolled batch-to-batch variation
· Long process development cycles
SynShell Core Advantages
· Parallel scale-up: micro-scale units × N
· Continuous feeding: eliminates batch variation
· In-line detection: real-time quality monitoring
· Modular design: rapid product changeover
· Seamless R&D-to-GMP transition

Full-Stack Delivery Capability Matrix

Encode · Materials
GreenMatrix™ — 100+ Green Material Systems, Three Core Domains(Monomers/crosslinkers、lipids/lipidoids、polymers/natural wall materials), ~100K Candidates Encoded as Unified Tokens, Unlocking a million-scale formulation design space.
Screen · AI + Microfluidics
OpenGMater delivery foundation model + ultra-high-throughput microfluidics platform — from formulation generation to μL-level rapid screening to real-time data feedback, the dry-wet experiment flywheel achieves order-of-magnitude efficiency gains.
Manufacture · Translation
SynShell™ Continuous-Flow Mfg — Four major process systems, parallel scale-up replaces traditional stepwise amplification, seamless path from lab to GMP. 5 hospital validations, 3 national-level programs.

We are not betting on any single drug — we are systematically building the delivery systems that make all next-generation therapeutics work better. From small-molecule sustained release to nucleic acid targeting to live biotherapeutics, SynX covers the full spectrum of delivery technology. We stand among the few global platforms integrating both micro- and nano-scale capabilities, with AI and ultra-high-throughput microfluidics as our engines to expand the frontiers of drug delivery.