As the number of protein targets deemed “undruggable” by small molecules continues to grow, antibody-based therapeutics have emerged as a powerful alternative, offering the precision and versatility needed to modulate a much wider range of targets. However, this promising new modality introduces additional complexity and a new set of challenges for labs already facing increasing pressure to accelerate workflows while maintaining quality and reproducibility. 

To address this, many labs are adopting automated liquid handling and robotics solutions that improve throughput, reduce manual error, and enable scaling of high-throughput screening (HTS), cloning, and expression testing. And they quickly discover that to use these tools effectively, they need robust software infrastructure.

For lab managers in antibody discovery programs, selecting software to drive and coordinate automated systems is a high-stakes decision. The right platform can streamline workflows, maintain data integrity, and boost collaboration across multidisciplinary teams. The wrong choice can cause inefficiencies, incompatibilities, and costly delays. With a growing number of options available, it’s essential to understand the key criteria for evaluating and selecting the right solution.

Precision and Scale in High-Throughput Screening

Antibody discovery is a data-intensive, iterative process that includes multiple stages: target identification, screening, cloning, expression, purification, and functional validation. By pairing automated liquid handlers with robotics, labs can execute hundreds to thousands of experiments simultaneously, dramatically expanding experimental capacity.  But without the right software, even the most advanced systems deliver limited value. The software must manage instrument control, scheduling, sample tracking, data integration, and exception handling across an often heterogeneous lab environment.

Inadequate or poorly integrated software can undermine even the most carefully planned investment in automation. To meet throughput goals, lab managers need to scale the hardware and operations. And to seamlessly orchestrate this hardware, they need a centralized, intuitive, and robust software platform.

Common Challenges in Automation and Robotics

Lab managers in automated labs face some common hurdles when scaling their operations:

  • Instrument Compatibility: Many labs use a mix of equipment from different vendors. Controlling and coordinating all these devices is difficult without custom integration or middleware.
  • Workflow Flexibility: Research and Development (R&D) workflows are not static. Many systems struggle to adapt to evolving protocols without heavy vendor intervention.
  • Data Management: High-throughput operations generate massive amounts of data, making sample tracking, metadata capture, and integration with other systems a complex logistical problem.
  • User Accessibility: Automation software must serve both automation engineers and bench scientists. Complex scripting requirements slow adoption and hinder collaboration.
  • Reliability and Support: Downtime in automated workflows is costly. Keeping systems running reliably while minimizing downtime for maintenance or onboarding new devices requires constant management.

These challenges underscore the need for a strategic approach to selecting the right antibody discovery software to drive automation.

Indispensable Features and Functionality for Antibody Discovery Software

To overcome these challenges and support antibody discovery at scale, software for automated liquid handling and robotics should include the following capabilities:

  1. Multi-Instrument Integration: Seamless control of diverse liquid handlers, robotic arms, plate readers, incubators, and other lab instruments through standardized drivers or open APIs (application programming interfaces).
  2. Visual Workflow Design: Drag-and-drop or GUI (graphical user interface) based workflow editors that allow scientists to build and adjust protocols without coding expertise.
  3. Sample and Data Tracking: Built-in support for barcoding, real-time sample tracking, and integration with laboratory information management systems (LIMS) and electronic lab notebooks (ELNs).
  4. Intelligent Scheduling: Dynamic resource scheduling that optimizes workflows based on equipment availability, queue prioritization, and time constraints.
  5. Error Handling and Alerts: Automated notifications for errors, deviations, or maintenance needs, with detailed logging for troubleshooting and compliance.
  6. Data Export and Analysis Integration: Easy export to common file formats and integration with downstream data analysis tools or bioinformatics pipelines. This is particularly important if your lab is also leveraging machine learning workflows.
  7. User Management and Compliance: Role-based access controls, audit trails, and support for compliance with regulations such as the US Food and Drug Administration’s (FDA) 21 CFR Part 11.

Together, these features allow software to meet your lab’s current needs and scale as your antibody discovery pipelines evolve.

Choosing the Right Software for Lab Automation

Selecting the right automation software has a ripple effect across the entire R&D workflow. Labs that implement an effective solution often report:

  • Increased Throughput: The ability to run more experiments in less time without compromising data quality.
  • Improved Data Integrity: Better sample tracking and fewer errors, resulting in cleaner, more reproducible datasets.
  • Faster Iteration Cycles: Rapid workflow design and modification that allows teams to adapt to results quickly.
  • Reduced Operational Costs: More efficient scheduling, minimal downtime, and lower total cost of ownership for automation systems.
  • Greater Cross-Functional Collaboration: Intuitive interfaces that enhance teamwork across disciplines and make automation accessible to both technical and non-technical users.

The combined effect of these improvements is a more agile, responsive, and productive antibody discovery program that’s better positioned to deliver high-quality therapeutic candidates.

Strategic Software Choices for Lasting Impact

Investing in the right software is more than just a technical decision. It’s a strategic move that can shape the trajectory of a drug discovery organization. For lab managers, the goal is to free scientists to focus on science, and software that supports transparency, scalability, and ease of use provides the foundation for innovation and growth.

As antibody discovery continues to grow in complexity and urgency, the role of automation, and the software that powers it, will only become more central. By making informed, forward-looking decisions, lab leaders can unlock the full potential of automated liquid handling and robotics, driving faster, smarter, and more reliable antibody discovery.