Dry Lab to Wet Lab Integration: Connect AI Models to Physical Lab Equipment
Stop losing insights between your AI predictions and lab executions. newLab® connects dry lab computational workflows to wet lab equipment operations, creating a true “lab in the loop” system for AI-driven research.
Bridge the Gap Between In Silico and In Vitro - Across Teams, Sites, and Systems
Whether you’re running AI-driven drug discovery, computational chemistry simulations, or bioinformatics workflows, there’s always a moment when dry lab predictions must become wet lab experiments. That’s where most organizations struggle: getting in silico results into the hands of bench scientists, then feeding in vitro data back to refine models.
newLab® eliminates this gap. By connecting your computational infrastructure to physical lab equipment management on ServiceNow, we create bidirectional data flow, a true “lab in the loop” system that enables AI-assisted research at scale.
Dry Lab Challenges
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Siloed Predictions
Model predictions stay stuck in computational environments, reaching bench scientists far too slowly for timely validation. -
Blind Capacity Planning
No visibility into whether the wet lab has the equipment or availability to validate computational results. -
Manual Protocol Handoffs
Weeks lost manually converting algorithmic outputs into executable lab procedures. -
Incomplete AI Training Data
AI models trained on equipment data that lacks the operational context needed for accurate predictions.
Wet Lab Disconnection
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Data Without Context
Equipment data lacks the metadata needed to be useful when fed back into computational models. -
Scheduling Ignores Priority
Manual scheduling doesn't account for which experiments are computationally urgent. -
Results Never Return
Experimental results stay trapped in notebooks, never making it back to data scientists who need them to refine models. -
Broken Feedback Loop
No mechanism to feed validated results back into models, so predictions never improve.
Key Features
Computational Workflow to Equipment Booking
AI predictions automatically trigger equipment reservation requests with priority scoring. Urgent model validations get immediate scheduling preference based on computational workflow requirements.
Equipment Data Feeds AI Models
Structured, normalized equipment data, including instrument parameters, measurement results, calibration history, and environmental conditions, flows directly to your computational infrastructure for high-quality AI model training with full lineage tracking.
Bidirectional Protocol Translation
Computational workflows convert into executable lab procedures while wet lab SOPs translate into computational pipeline parameters, with complete version control across both environments.
Cross-Platform Visibility
Data scientists gain real-time visibility into wet lab capacity and equipment availability. Lab scientists receive computational predictions with experiment recommendations. IT Directors manage everything through a unified ServiceNow platform.
Closed-Loop Feedback System
Experimental results automatically update computational models while model performance metrics trigger equipment calibration needs. This creates a continuous improvement cycle where predictions and validations constantly inform each other.
How Leading Labs Transformed Their Operations
Trusted by leading pharma and biotech companies to streamline lab operations, improve efficiency, and optimize resource utilization.
Frequently Asked Questions
How does newLab® connect computational and physical lab systems?
newLab® acts as the operational bridge between computational platforms and the physical laboratory. It integrates with AI models and ELN systems to receive experiment requests, then orchestrates the required lab resources to execute them. During execution, it captures operational metadata and links it to the experiment context. This ensures that experimental results flow back into AI and analytics systems, enabling continuous Lab-in-the-Loop learning cycles.
Can newLab® integrate with our existing AI/ML platforms like Domino, SageMaker, or Databricks?
Yes. newLab® integrates with major computational platforms through APIs and data pipelines. We support connections to AWS SageMaker, Domino Data Lab, Databricks, Azure ML, and custom HPC environments. Our integration team works with your data scientists and IT team to establish secure, automated data flows that respect your existing computational architecture.
What data flows from wet lab equipment back to AI models?
newLab® captures several types of equipment metadata for AI consumption: instrument models, control software versions, calibration history, maintenance events that affect data quality, and usage patterns. All data is structured, timestamped, and tagged with equipment IDs so your AI models receive high-quality training data with full lineage tracking.
How does newLab® handle data quality issues that could corrupt AI models?
newLab® implements multiple quality control layers: automated flagging of out-of-calibration equipment and documentation of all maintenance events that could affect data reliability. Data scientists receive quality scores with all equipment data, allowing them to filter training datasets and weight results appropriately.
Do we need separate systems for dry lab and wet lab, or does newLab® replace both?
newLab® focuses on wet lab equipment management and the orchestration layer to dry lab systems. It doesn’t replace your computational infrastructure (HPC, AI/ML platforms) or specialized scientific tools (LIMS, ELN). Instead, it acts as the intelligent scheduling engine that keeps data flowing between computational predictions and physical experiments. Think of newLab® as the orchestration layer that makes your existing dry lab and wet lab systems work together.
Can computational scientists book equipment directly from their workflows?
newLab® focuses on wet lab equipment management and the orchestration layer to dry lab systems. It doesn’t replace your computational infrastructure (HPC, AI/ML platforms) or specialized scientific tools (LIMS, ELN). Instead, it acts as the intelligent scheduling engine that keeps data flowing between computational predictions and physical experiments. Think of newLab® as the orchestration layer that makes your existing dry lab and wet lab systems work together.
How do you handle data security between computational and physical lab systems?
All data flows through ServiceNow’s enterprise-grade security infrastructure, which supports role-based access control, encryption in transit and at rest, audit logging of all data access, and configurable data governance policies. Your IT team controls exactly what data flows between systems, who can access it, and how long it’s retained.
Close the Dry Lab to Wet Lab Loop in Your Pharma & Biotech Research
Stop wasting computational insights and experimental capacity. Connect your dry lab and wet lab for true AI-driven research.