AI-driven drug discovery at scale

What we'd propose

• Digital Lab

  MTP-Based Modular Laboratory Architecture

  We implement MTP (VDI/VDE/NAMUR 2658) standard across Adamed Discovery's bioreactor, chromatography, and analytical equipment ecosystem — enabling Plug and Produce modularity where new instruments integrate into the Labgears platform without custom programming, and every equipment vendor's communication protocol is handled by the standardised MTP interface layer.

  • MTP equipment module library for all instrument types

    Every instrument type has a standardised MTP integration module

    Define MTP-compliant equipment modules for the bioreactors, chromatography systems, UPLC/MS, and cell counters in the Kajetany and Pienkov laboratories — each module handles the vendor-specific communication protocol and exposes a standardised interface to the Labgears platform.

  • Plug and Produce commissioning for new instruments

    New instruments commissioned in hours rather than weeks

    Configure the Plug and Produce commissioning workflow — when a new instrument is added to a laboratory, the MTP module is loaded and the instrument is operational in the Labgears platform within hours rather than requiring the custom integration engineering that currently delays new equipment deployment.

  • Cross-site MTP deployment for Pabianice pilot plant

    Pabianice pilot bioreactors connected via MTP standard

    Extend the MTP standard deployment to the Pabianice 50L mammalian and 100L bacterial pilot bioreactors — connecting the pilot plant to the same Labgears platform architecture used in the Kajetany and Pienkov research laboratories.

  • The computational biology team can access data from any instrument through the standardised MTP interface — the AI model training datasets are no longer limited by the instruments that have custom integrations.
  • New equipment is deployed faster because MTP-compliant modules eliminate custom integration engineering — the laboratory expansion plans are not delayed by the integration work that currently follows every new instrument purchase.
  • The Labgears platform scales with the organisation because new capabilities are added through module configuration rather than custom development — the platform grows with the drug discovery programme without accumulating integration debt.

  We've done this before

  • MTP Library for Modular Automation
  • Integration of QB Modules with Siemens PCS 7

  See how we do Digital Lab
• Digital CDMO

  Secure OT Gateway and Zero Trust Architecture

  We deploy secure IoT gateways and Zero Trust network architecture for Adamed Discovery's legacy OT environment — connecting air-gapped manufacturing equipment to the central data platform while enforcing identity-aware access controls, protecting automated production lines from the cyber threat vectors that target pharmaceutical manufacturing facilities.

  • Zero Trust OT gateway deployment

    Legacy OT equipment connected with identity-aware security controls

    Deploy Zero Trust OT gateways at the boundary between the Pabianice and Ksawerow OT networks and the enterprise IT systems — each device is authenticated before receiving network access, and the principle of least privilege restricts what each authenticated device can communicate with.

  • Continuous OT security monitoring and alerting

    OT network anomalies detected and alerted in real time

    Configure OT security monitoring that tracks network traffic patterns in the Pabianice and Ksawerow manufacturing networks — identifying anomalous communication patterns that may indicate a cyber intrusion and alerting the security operations team before the threat propagates.

  • Secure remote access for OT equipment maintenance

    Maintenance engineers access OT systems with full audit trails

    Implement secure remote access for equipment vendors and internal maintenance engineers — each remote access session is authenticated, logged, and audited, enabling remote maintenance support without creating persistent remote access vulnerabilities.

  • The OT network is protected from the lateral movement attacks that have compromised other pharmaceutical manufacturing facilities — the Zero Trust architecture ensures that a compromised IT system cannot be used as a pivot point to reach OT control systems.
  • Remote maintenance support is enabled without the security risk of persistent remote access — equipment vendors can diagnose and resolve issues remotely with full audit trails, reducing on-site visit requirements and the associated travel costs.
  • The cybersecurity posture is demonstrable to pharmaceutical clients and regulatory auditors — the NIS2 and IEC 62443 compliance requirements are met through the architectural controls rather than through after-the-fact documentation.

  We've done this before

  • Support Framework & Assisted Reality for Global Biotech Manufacturer
  • Lifecycle Management for Adaptive Biotech Assets

  See how we do Digital CDMO
• Enterprise AI

  Digital Twin for ONCO51 Bioprocess Optimisation

  We develop physics-based and data-driven digital twin models for Adamed Discovery's ONCO51 biologics manufacturing process — enabling virtual experimentation to identify optimal growth conditions, predict scale-up behaviour, and reduce the physical experimental cycles required to characterise the manufacturing process for Phase 2 clinical supply.

  • Cell culture digital twin for mammalian bioprocess

    ONCO51 cell culture behaviour predicted in simulation

    Build the cell culture digital twin for the ONCO51 mammalian expression system — combining first-principles models of cell metabolism with empirical data from the Kajetany bioreactor runs to predict cell growth, viability, and product quality under varied process conditions.

  • Scale-up simulation from 1L to 50L bioreactors

    50L scale performance predicted from 1L research data

    Configure the scale-up simulation models that predict 50L pilot bioreactor performance from 1L research bioreactor data — identifying the mixing, oxygen transfer, and shear stress parameters that must be controlled differently at scale to maintain product quality.

  • Digital twin validation and continuous model updating

    Digital twin accuracy verified with every physical batch

    Build the model validation pipeline that compares digital twin predictions against actual batch results — continuously recalibrating the model parameters as more physical data accumulates, improving prediction accuracy with each completed batch.

  • The ONCO51 clinical supply timeline is protected because scale-up risks are identified and addressed in simulation rather than through physical trial batches — the Phase 2 manufacturing process is characterised faster and with fewer failed batches.
  • Manufacturing process development costs are reduced because fewer physical experiments are required to characterise the operating parameter space — the experimental budget for ONCO51 is stretched further because each physical experiment is informed by simulation results.
  • The digital twin platform becomes an asset for subsequent biologics programmes — the SHIELD and INHAPRO development timelines benefit from the same modelling infrastructure, multiplying the value of the initial platform investment.

  We've done this before

  • Real-Time Process Intelligence & KPI Visualization
  • Automated Cell Line Control with Advanced PAT

  See how we do Enterprise AI
• Digital Lab

  AI Vision Systems for Microbiology Laboratory Automation

  We implement AI-driven computer vision systems for Adamed Discovery's expanded microbiology laboratory — automated cell counting, classification, and early contamination detection that enables the fourfold laboratory capacity expansion to deliver its intended productivity improvement without proportional headcount expansion.

  • Automated cell counting with computer vision

    Cell density measured automatically from microscopy images

    Deploy computer vision models for automated cell counting from microscopy images — eliminating the manual counting step that currently limits microbiology laboratory throughput and introducing the objective, reproducible measurement that automated process control requires.

  • Contamination detection and early warning alerting

    Contamination identified from image analysis before it spreads

    Train contamination detection models on historical microbiology image data — identifying the visual signatures of bacterial and fungal contamination in cell culture before the contamination becomes visible to the naked eye or spreads to other culture vessels.

  • Microbiology results integrated with LIMS and batch records

    Microbiology results flowing automatically to LIMS and batch records

    Configure the integration between the computer vision system and LIMS — microbiology results are captured, attributed, and transferred to the batch record automatically, eliminating the manual transcription step that currently creates ALCOA+ compliance risks.

  • The fourfold microbiology capacity expansion delivers its intended productivity improvement — the laboratory throughput scales with the investment rather than being limited by manual counting throughput.
  • Contamination-related batch failures decrease because contamination is detected earlier — the ONCO51 production batches are protected from the culture failures that would delay the clinical trial programme.
  • Microbiologist time is redirected from monotonous counting to interpretation and investigation — the skilled microbiology team focuses on the analytical work that requires human expertise rather than on repetitive measurement tasks.

  We've done this before

  • Bioreactor Control: Computer Vision for Non-Invasive Foam Management
  • Fully Automated Autosampling Control System

  See how we do Digital Lab
• Enterprise AI

  Ontology-Driven Scientific Data Platform

  We build an ontology-driven Industrial Data Platform for Adamed Discovery that unifies scientific data across all 19 therapeutic areas — semantic data structures that connect molecular design, assay results, bioprocess parameters, and clinical outcomes in a machine-readable knowledge graph, providing the ML-ready data foundation that the TechBio computational biology mission requires.

  • Biological ontology framework for drug discovery data

    Molecular designs, assay results, and process parameters in one ontology

    Define the ontology framework that structures Adamed Discovery's drug discovery data — connecting genetic sequences, protein expression measurements, assay results, bioprocess parameters, and clinical outcomes in a biologically meaningful knowledge graph that enables cross-area queries.

  • Automated data ingestion from Labgears and external databases

    All data sources connected to the ontology platform automatically

    Build automated data pipelines from Labgears, external sequence databases, clinical trial databases, and partner data systems — every data source that contributes to drug discovery decisions is connected to the ontology platform with automated quality checks at ingestion.

  • ML-ready data warehouse with automated feature engineering

    Data prepared for ML model training automatically

    Configure the ML-ready data warehouse that structures the ontology data for machine learning applications — automated feature engineering pipelines prepare the data for model training without requiring the data engineering effort that currently slows every new ML project.

  • The computational biology team can pursue ML projects without the months of data engineering that currently precede every model development effort — the ML-ready data warehouse reduces the barrier to applying AI to drug discovery problems.
  • Cross-therapeutic-area insights are possible because the ontology connects data across all 19 areas — patterns that would be invisible in individual programme datasets become apparent when the full portfolio is queried.
  • The data platform accelerates the TechBio mission transition — the biological data assets that are built through this platform become a compounding strategic advantage as the breadth and depth of the ontology grows.

  We've done this before

  • Ontology-Driven Ecosystem for Biomanufacturing Data
  • Transforming Raw Bioprocess Data into Actionable Insights

  See how we do Enterprise AI