Automation in 2026 is no longer judged by the volume of experiments, but by the reliability of the evidence they produce. As complex biology and tighter budgets collide, industry leaders are pivoting toward automated workflows to secure the data integrity required for confident, early-stage decision-making.
Automation is well-established in early drug discovery, but its growing importance in 2026 reflects practical constraints rather than incremental uptake of laboratory hardware. Large pharmaceutical companies and well-funded biotechs are applying automation more deliberately as biological models become more complex, data volumes increase and expectations around cost and time to decision tighten.
What distinguishes the current phase is not wider access to robotics, but a change in where automation is applied. Rather than focusing primarily on throughput, organisations including AstraZeneca, as well as a growing number of platform biotechs, are using automation to address reproducibility, data integrity and early experimental decision-making.
Pressure points in early discovery
Despite advances in screening technologies and computational methods, early drug discovery continues to show high attrition. Programmes frequently fail because early experimental evidence is insufficient, inconsistent or difficult to reproduce across laboratories and time points. These weaknesses are often compounded as projects progress, increasing the risk of late-stage failure.
In 2026, these limitations will likely become more visible as discovery teams adopt more complex biological systems. Cell-based assays, phenotypic screening and patient-derived models generate large datasets but are sensitive to technical variation. Organisations working with complex 3D and co-culture systems report that manual workflows struggle to deliver the consistency required for confident prioritisation.
Automation is being applied to reduce this variability by standardising assay preparation, execution and data capture. The aim is not simply to increase experimental output, but to generate datasets that remain interpretable when integrated with downstream computational analysis.
Where automation is being applied now
High-throughput screening remains a major application of laboratory automation, particularly in compound screening and profiling. Beyond this, automation is also being used to support more complex experimental formats that are difficult to execute consistently using manual workflows. These include studies requiring multiple experimental conditions, concentration ranges and repeated measurements over time, where small differences in handling or timing can affect outcomes. In these workflows, high-accuracy analysis is critical. For example, specialised tools such as ORYL Photonics’ laser-based solubility platforms, ensure that physical properties are accurately captured at scale, preventing poor compound data from skewing early results.
Platform biotechs such as Recursion Pharmaceuticals and Insitro have built discovery models around large-scale automated experimentation combined with high-content imaging and machine learning. While these approaches do not eliminate biological uncertainty, they allow experiments to be run with a level of consistency that is unattainable through manual methods.
Data capture and experimental traceability
Automation is also being adopted to address long-standing challenges around data capture and documentation in early discovery. Many automated platforms are designed to record experimental parameters and metadata alongside assay results, supporting traceability and comparison across experiments.
This has become more important as assays are frequently modified during early development. Without reliable records of protocol changes, reagent sources and execution conditions, results can be difficult to interpret or reproduce. Industry discussions around laboratory automation frequently highlight structured data capture as a key benefit for reproducibility and downstream analysis.
For organisations linking laboratory outputs with computational models, consistent and machine-readable datasets are increasingly important. Automation supports this by aligning experimental workflows with analytical requirements and reducing reliance on manual data handling. By adopting standardised data schemas, organisations are ensuring that laboratory outputs are not just captured, but are immediately ‘AI-ready’ for cross-platform analysis.
Integration with computational workflows
Entering 2026, several discovery organisations are seeking closer integration between automated experimentation and computational chemistry, modelling and data science workflows, with the aim of reducing delays between experimental execution and analysis.
Recent platform-level collaborations reflect this direction. In January 2026, Eli Lilly announced that its AI-based drug discovery platform, TuneLab, would be integrated into Schrödinger’s LiveDesign informatics software. Through this partnership, biotech companies will gain access to TuneLab’s AI tools within the LiveDesign environment, supporting faster drug discovery workflows and broader availability of AI-driven modelling.
Economic reality: doing more with less
Economic conditions continue to influence automation strategies. While large pharmaceutical companies have largely maintained infrastructure investment, many biotechs are operating under tighter budgets following a prolonged period of venture capital volatility. As a result, discovery teams are under pressure to validate targets without expanding headcount, making automation a key route to increasing experimental capacity.
For smaller companies, the high upfront cost of fully integrated automation platforms remains a barrier. In response, some are favouring access models that reduce capital commitment, including:
- Modular systems: Discrete, configurable units that can be added as programmes progress
- Shared platforms: Collaborative laboratory environments that provide access to advanced robotics without full internal ownership
- Specialist partnerships: Use of contract research organisations (CROs) such as Evotec and Charles River Laboratories, which offer automated discovery capabilities as part of broader collaboration or service models.
Outlook: embedding trust into discovery
Automation in early drug discovery has moved from improving throughput to enforcing experimental quality. At industry forums such as ELRIG’s Drug Discovery meetings, discussion has moved beyond laboratory hardware to standardised execution and structured data capture. This demonstrates a growing consensus that automation is critical to improving reproducibility in drug discovery.
Ultimately, the impact of automation depends less on individual tools and more on how effectively these systems are embedded within the experimental strategy and analytical pipelines. When integrated correctly, laboratories move beyond simply running experiments to generating high-fidelity evidence that supports confident, early-stage decision-making.
Where discovery scales
The themes discussed in this article are examined in greater depth in our latest Beyond the Lab report: Lab Automation: Where Discovery Scales. This comprehensive analysis features exclusive perspectives from a diverse group of leaders across the automation and discovery sector:
- Rob Howes, Senior Director, Charles River
- Lukas Gaats, Co-founder and CEO, mo:re
- Stuart R Green, Staff Scientist, Acceleration Consortium, University of Toronto
- Fabian Gerlinghaus, CEO, Cellares
- Dr Hiroaki Yamanaka, Principal Scientist, Yokogawa
- Nick Randall, Senior Director, Repligen
- Dr Nathan Dupertuis, Co-founder, ORYL Photonics
Register free to access the full report.
