Pharmaceutical Reactor Intelligence · Beta

Match Every
Molecule
to Its Reactor

Agentic AI platform that converts pharmaceutical reaction chemistry into optimized reactor decisions — vendor-neutral, ICH Q11 compliant, in under 8 minutes.

⚗ Explore POC Finder View Platform
94%
Reactor Match Accuracy
< 8m
Full Spec Generation
18×
Faster than Manual
✓ ICH Q11 Compliant
molecueai · reactor-finder
SMILES Input
CC(C)Cc1ccc(cc1)C(C)C(=O)O
IBF-7
⚗Process AgentDone
🔬Reactor Design AgentRunning
🌀Mixing & Heat AgentQueued
⚠Risk AgentQueued
✅Vendor Validation AgentQueued
AI Recommendation
Continuous Stirred Tank Reactor (CSTR)
✓ Confidence 94% · Scale-up Risk: Low
Yield
91%
Safety
96%
Scale-up
87%
Recognised by
Industry Problem

Reactor Selection is Still Guesswork

Pharma manufacturers spend weeks selecting reactors using gut feel, Excel sheets, and vendor relationships — not data. The result is costly scale-up failures, regulatory delays, and millions lost.

Senior GLR engineers are retiring, taking irreplaceable process knowledge with them. No AI system exists today to convert reaction chemistry into validated reactor specifications.

23%
of pharma scale-up failures caused by poor reactor selection — costing $2–5M per incident. India spends ₹45,000Cr in pharma CAPEX annually with zero AI decision support.
🧠
Knowledge Locked in Experts
Senior GLR engineers carry decades of process knowledge. No systematic capture exists. When they retire, that knowledge is permanently lost, affecting every future project.
📊
Excel-Based, Error-Prone Process
Reactor sizing calculations run on error-prone spreadsheets. Vendor bias distorts procurement. No cross-project learning accumulates across the organisation.
⏳
3–6 Weeks per Decision
A single reactor specification review takes weeks of back-and-forth between engineers, vendors, and regulators. Competitor speed is now a clinical trials advantage.
🔥
Scale-Up Failures Are Costly
Cooling failures, poor mixing, and cleaning incompatibilities discovered at pilot scale cost tens of crores per batch failure and delay FDA submissions.
Our Solution

Agentic AI for Reactor Intelligence

A five-agent AI system that ingests reaction chemistry and outputs a complete, validated reactor specification — in minutes, not weeks.

01 / 04🔬
Molecule Ingestion
Accept SMILES, InChI, or SDF. Extract 200+ molecular descriptors via RDKit and ChemBERTa embeddings automatically.
02 / 04⚗
Reactor Mapping
Map molecular properties to reaction conditions — temperature, pressure, mixing, catalyst — identifying compatible reactor classes.
03 / 04🛡
POC Screening
Rank all compatible reactors by yield, safety, scale-up risk, and cost. Generate ICH Q11 compliant POC reports instantly.
04 / 04🎯
Optimal Selection
Multi-criteria AI scores yield, heat transfer, safety, regulatory compliance, and vendor neutrality to deliver the single best reactor.
Process

From SMILES to Reactor Spec in 8 Minutes

Five AI agents work in parallel, each specialising in a domain of pharmaceutical reactor engineering.

01
Molecular Input & Parsing
Submit SMILES, SDF, or InChI key. RDKit generates Morgan fingerprints, MACCS keys, and 200+ physicochemical descriptors. ChemBERTa encodes the molecule into a 768-dimensional embedding.
02
ADMET Property Prediction
ChemBERTa predicts absorption, distribution, metabolism, excretion, and toxicity. XGBoost classifies reaction regime, thermal stability, and hazard level.
03
Reaction Condition Mapping
AI maps predicted properties to operating conditions — temperature window, pressure sensitivity, catalyst compatibility, mixing intensity, and cleaning requirements.
04
Multi-Objective Reactor Scoring
MCDM engine scores all compatible reactor types across yield, heat/mass transfer, safety compliance, scale-up risk, and ICH Q11 regulatory alignment.
05
POC Report & Vendor Matching
Download a structured report with recommended reactor, operating parameters, risk flags, vendor shortlist, and complete scale-up pathway.
Platform Performance● Live
< 8 min
SMILES → full reactor specification
Reactor Match Accuracy94%
Yield Prediction89%
Safety Flag Detection97%
Scale-up Risk Index91%
Vendor Neutrality99%
Platform

Four Modules, One Intelligence Layer

MolecueAI is built as a modular platform — each capability works standalone or as part of an end-to-end reactor intelligence workflow.

Module 01
🔬
Molecule Ingestion & Profiling
Accept SMILES strings, InChI keys, or SDF files. Automatically extract 200+ physicochemical properties using RDKit fingerprinting and ChemBERTa transformer models.
  • SMILES, InChI, SDF input formats
  • Morgan fingerprint + MACCS key generation
  • ChemBERTa 768-dim molecular embedding
  • Batch processing via REST API
Module 02
⚗
Reactor Process Mapping
Map molecular properties to reaction conditions — temperature, pressure, residence time, mixing — identifying which reactor class is chemically compatible with your compound.
  • Reaction condition inference engine
  • Thermal stability classification
  • Catalyst compatibility scoring
  • Hazard & exothermicity flag detection
Module 03
🧪
POC Reactor Screening
Run proof-of-concept screens across your reactor library. Rank candidates by predicted yield, safety profile, scale-up feasibility, and cost — with instant report generation.
  • Multi-reactor comparative ranking
  • Yield & selectivity prediction
  • Safety score computation
  • IQ/OQ/PQ checklist generation
Module 04
🎯
Optimal Reactor Identification
Multi-criteria AI scoring combines reaction kinetics, heat/mass transfer, safety constraints, and ICH Q11 regulatory compliance to select the single best reactor for scale-up.
  • MCDM scoring engine
  • ICH Q11 compliance verification
  • GMP-ready configurations
  • Scale-up risk index & pathway
Reactor Intelligence

Six Reactor Classes, Fully Characterized

MolecueAI understands the process chemistry, engineering constraints, and scale-up profile of every major pharmaceutical reactor type.

Continuous Flow🔄
CSTR — Continuous Stirred Tank Reactor
  • Liquid-phase reactions, ideal for APIs
  • High mixing efficiency, easy T-control
  • Residence time: 5–120 min pilot scale
  • Scale-up risk: Low
Continuous Flow➡
PFR — Plug Flow Reactor
  • Fast kinetics, no back-mixing
  • High selectivity, tubular configuration
  • Scalable in modular parallel units
  • Scale-up risk: Moderate
Batch🤹
Batch Reactor
  • Multi-step complex chemistry
  • Industry standard, GMP-ready
  • Variable reaction time, flexible
  • ICH Q11 compliant configurations
Microfluidic💧
Microreactor
  • Hazardous chemistry — safe POC
  • Ultra-fast heat transfer
  • Milligram-scale API screening
  • Photochemistry capable
Hybrid🌀
Semi-Batch Reactor
  • Controlled exothermic reactions
  • Selective conversion, safe dosing
  • Common in pharma API synthesis
  • Scale-up risk: Moderate
Electrochemical⚡
Electrochemical Cell Reactor
  • Oxidation/reduction routes
  • Green chemistry — no oxidants
  • Emerging pharma applications
  • POC validation supported
Pilot Molecules

Molecule Library & POC Reactor Finder

Browse our profiled pilot molecule database and use the interactive tool to identify the optimal reactor for your compound instantly.

Molecule / API
MW (Da)
logP
AI Score
Ibuprofen Analog IBF-7
CC(C)Cc1ccc(cc1)C(C)C(=O)O
206.3
3.97
92
Simvastatin-class STV-3
O=C(O)C[C@@H]1CC(=O)O...
418.6
4.68
87
Paclitaxel Precursor PTX-P
OC(=O)c1ccccc1NC(=O)...
853.9
3.96
74
Azithromycin Scaffold AZM-S
CC[C@@H]1OC(=O)[C@H](C)...
748.9
4.02
88
Metformin Derivative MTF-9
CN(C)C(=N)NC(=N)N
173.2
-1.43
96
Kinase Inhibitor KI-14
c1ccc(-c2nc3ccccc3s2)cc1
345.4
5.12
79
⚗ POC Reactor Finder
Select a molecule class for an instant AI reactor recommendation with full operating parameters.
Recommended Reactor
    AI Engine

    Three-Stage AI Pipeline

    Each molecule processed through a structured three-stage pipeline, returning a scientifically justified reactor recommendation with full confidence scoring.

    Stage 01 / 03
    🧬
    Molecular Descriptor Extraction
    RDKit generates Morgan fingerprints, Tanimoto similarity vectors, and 200+ molecular descriptors. ChemBERTa encodes SMILES into a 768-dimensional embedding capturing reactivity context and functional group semantics.
    RDKitMorgan FPChemBERTaSMILES
    Stage 02 / 03
    ⚙
    Reaction Condition Inference
    Fine-tuned XGBoost classifier maps descriptor vectors to reaction condition spaces — predicting temperature windows, pressure sensitivity, mixing requirements, catalyst compatibility, and hazard flags including exothermicity and thermal instability.
    XGBoostADMETThermal AISafety AI
    Stage 03 / 03
    🏆
    Multi-Objective Reactor Scoring
    Multi-criteria decision model scores all compatible reactor types across yield potential, heat/mass transfer adequacy, safety compliance, scale-up risk index, and ICH Q11 regulatory alignment — returning top-ranked reactor with confidence score.
    MCDMICH Q11GMPScale-up AI
    Technology

    Built on Enterprise-Grade Infrastructure

    Modern, scalable stack combining cheminformatics, transformer AI, cloud infrastructure, and pharmaceutical-grade reliability.

    ⚛
    Next.js
    Frontend UI
    🔷
    .NET Core 8
    API Gateway
    🐍
    Flask/Python
    AI Services
    🧪
    RDKit
    Cheminformatics
    🤗
    ChemBERTa
    Molecular AI
    📊
    XGBoost
    Scoring Model
    🐘
    PostgreSQL
    Molecule DB
    🔴
    Redis
    Cache Layer
    ☁
    AWS / Azure
    Cloud Infra
    📱
    Flutter
    Mobile App
    🐳
    Docker/K8s
    Deployment
    🔒
    JWT/OAuth2
    Auth & Security
    Why MolecueAI

    Enterprise-Grade Pharmaceutical Intelligence

    Built for research labs, pharma manufacturers, and CDOs who need reliable, fast reactor decisions at every development stage.

    🔒
    GMP & ICH Q11 Compliant
    All recommendations validated against ICH Q11 guidelines and GMP-ready configurations. Regulatory-grade reports with full audit trail in every POC output.
    ⚡
    8-Minute Decisions
    From SMILES input to full reactor specification in under 8 minutes. Eliminate weeks of manual screening and expert consultation for routine reactor selection.
    🔬
    Scientifically Rigorous
    Powered by RDKit, ChemBERTa, and XGBoost trained on curated pharmaceutical reaction datasets. Every recommendation includes confidence scores and full justification.
    📈
    Scale-Up Intelligence
    Models scale-up feasibility from milligram to kilogram, predicting risks and parameter windows for each scale — not just a one-time POC recommendation.
    ⚖
    Vendor Neutral Always
    Objective multi-vendor comparison based on technical performance data, not relationships. Every recommendation free from commercial bias — by design.
    🌐
    REST API & Integrations
    Full REST API for integration with LIMS, ELN, SAP, and research data management systems. Cloud or on-premise deployment available.
    About Us

    Built by Domain Experts, Powered by AI

    MolecueAI was built at the intersection of deep pharmaceutical process engineering expertise and cutting-edge agentic AI — two disciplines that rarely meet in one company.

    We were built by a GLR process engineering veteran with 30+ years of hands-on reactor design and commissioning experience, and a PhD AI researcher with 33 years of enterprise technology leadership.

    Together we are building the intelligence layer that every pharmaceutical plant, equipment vendor, and engineering consultant in the world will rely on to make reactor decisions — starting with GLR, expanding to full plant equipment AI.

    Our Mission
    “Digitize the process knowledge of the world’s best reactor engineers into AI that any pharma team can access — in seconds, not weeks.”
    ME
    Murugan E.A
    Chief Product Architect
    B.E. · M.B.A. · GLR Process Expert
    30+ years of hands-on GLR and pharmaceutical reactor engineering across API manufacturing, scale-up, and vendor evaluation. Has designed, commissioned, and validated reactor systems for leading pharma companies. Deep expertise in reaction engineering, heat transfer, mixing, and cleaning validation — the domain backbone of MolecueAI's AI decision engine.
    GLR EngineeringReactor DesignScale-upIQ/OQ/PQAPI ManufacturingVendor Management
    CS
    Dr. Sashi C.S
    Chief AI Architect
    M.C.A. · M.S. · Ph.D. · 33 Yrs Experience
    PhD in Computer Science with 33+ years of enterprise technology leadership across BFSI, healthcare, and SaaS. Published 8 ML/AI research papers. Expert in Agentic AI, RAG systems, Claude-based orchestration, .NET Core 8, and large-scale enterprise architecture. Brings the AI engineering depth that makes MolecueAI's decision engine production-grade and pharma-reliable.
    Agentic AIClaude CodeRAG SystemsChemBERTaRDKit.NET Core 8Enterprise Arch.
    Seed Round

    Seeking $500K – $1M Seed Funding

    Pre-revenue · DPIIT Startup India · Convertible Note or Equity · Chennai, India

    35%
    $175,000
    Product Engineering
    • Full 5-agent system
    • Claude Code integration
    • Next.js + .NET Core
    • Flutter mobile app
    30%
    $150,000
    Pilot Programs
    • 3 pharma pilots
    • Onboarding & integration
    • Customer success
    • Feedback & iteration
    20%
    $100,000
    AI Development
    • Model training
    • Knowledge graph
    • Claude API costs
    • Research partnerships
    15%
    $75,000
    Operations
    • Legal & IP filing
    • Office & infrastructure
    • Team & advisory
    • Marketing & BD
    ⚗ Schedule a Demo
    Get Started

    Ready to Find Your Optimal Reactor?

    Run your first pilot molecule through MolecueAI's POC reactor identification engine. No setup required. Full report in under 8 minutes.

    📩 Contact Sales