5 LIMS & Lab Automation


There are techniques like SMILES strings, but it can be hard to canonically describe molecules using this technique. These molecules lack the clear ‘beginning’ and ‘end’ sequences that characterise proteins and genetic codes, and their 3D representations are not standardised. As a result, chemists must develop new AI techniques that can effectively process molecular data. The data required to train AI models is also scarce and fragmented, often hidden in decades of academic journals and custom diagrams that are not easily amenable to analysis.


New approaches to AI in chemistry:


Graph neural networks One promising AI technique for chemistry is graph neural networks (GNNs), which have shown great potential in predicting chemical reactions. GNNs operate by representing molecules as graphs, where atoms are nodes, and bonds are edges. This method allows the network to learn about the relationships between nearby atoms and predict key properties such as reactivity and toxicity. As Dr Emma King-Smith, Chancellor’s Fellow at the University of Edinburgh, explains: “Chemistry is a really interesting fi eld because our models for reactivity are pretty rudimentary, but chemists are shockingly good at getting reactions to work. I think what this says is that we have a solid foundation of the chemistry basics, but there’s still a lot of intuition involved.” Dr King-Smith’s research has explored using message-passing neural networks to predict important chemical properties, even with limited data. “Even with less than 1,000 data points, it can predict a vast array of important chemistry, such as acute toxicity, odour profi le, and how well a molecule will perform in chemical reactions,” she notes.


The future of AI in laboratory research


The potential of AI in both biological and chemical research is vast. In genomics, LLMs and the more specialised gLMs are helping researchers decode genetic information and uncover hidden patterns, which can lead to breakthroughs in areas like personalised medicine and crop engineering. In chemistry, AI models are beginning to assist in understanding molecular reactivity, predicting reaction outcomes, and even designing new compounds with specifi c properties. As Dr King-Smith puts it: “From one model, we can see such a variety in the prediction tasks, which suggests that AI has the potential to revolutionise many facets of chemical research.”


While challenges remain - such as the need for larger datasets, better model interpretability, and overcoming domain-specifi c complexities - AI is undeniably opening


new frontiers in laboratory research. By continuing to refi ne these models and exploring novel applications, AI has the potential to transform the way we understand and manipulate the natural world. The integration of AI into laboratory practices is not just a trend, but a fundamental shift that will accelerate discoveries and optimise workfl ows across disciplines.


Conclusion


As AI technologies continue to evolve, their applications in laboratory research are expanding rapidly. From genetic sequence analysis to the modelling of complex chemical reactions, AI is enabling scientists to push the boundaries of what is possible. By automating tedious tasks, uncovering hidden patterns, and providing new ways to simulate and predict outcomes, AI has the potential to revolutionise both the biological and chemical sciences, paving the way for faster, more efficient discoveries that could have a lasting impact on fields ranging from medicine to agriculture and beyond.


About the author


Oliver holds a PhD in Mathematics from UC Berkeley and an executive MBA from Stanford, and is an innovator with expertise in Data Visualization, Statistics, Machine Vision, Robotics, and AI. As a serial entrepreneur, he has founded three companies and contributed to two successful exits. At his latest company, smartR AI, Oliver King-Smith spearheads innovative patent applications harnessing AI for societal impact, including advancements in health tracking, support for vulnerable populations, and resource optimisation. Throughout his career, Oliver has been dedicated to developing cutting- edge technology to address challenges, and today smartR


AI is committed to providing safe AI programs within your own secure and private ecosystems.


LinkedIn profi le: https://www.linkedin.com/in/oliverkingsmith/ Email: oliverks@smartr.ai


Fully integrated lab automation platform


Trilobio, a developer of whole- laboratory automation solutions, has introduced the first version of its integrated robotics, lab equipment, and software platform. Early pilot data highlights its ability to enhance research workflows by improving efficiency, data quality, and reproducibility.


Founded to advance genetic engineering, synthetic biology, and life science research, Trilobio has built modular robotic lab automation with an integrated application store for packaging and distributing lab protocols as code. By automating entire lab workflows, the company addresses critical issues in research reproducibility, with studies estimating that 77% of biologists struggle to replicate their own or others’ experiments, despite existing automation tools.


The Trilobio platform includes the Trilobot lab robot, research devices (grippers, pipettes, and tube handlers), and Trilobio OS, a research protocol software designed to ensure reproducibility across all Trilobio-enabled labs. By automating whole-lab operations, Trilobio helps researchers maximise efficiency, accuracy, and reliability while reducing the cost and complexity of adopting automation.


At the core of the platform is Trilobot, a multifunctional robot built on standardised hardware and software, allowing seamless operation of any Trilobio research tool. Multiple Trilobots can collaborate to scale up experiments. Trilobio OS serves as the platform’s software engine, integrating protocol design, optimisation, and execution with an automated lab notebook and LIMS. Researchers can create advanced protocols using a no-code graphical interface instead of machine code, making automation more accessible. The system also optimises protocols for speed, cost, or accuracy automatically.


By combining standardised hardware and innovative software, Trilobio ensures that protocols executed on its platform, are fully reproducible across any configured Trilobot system, without recalibration or rewriting.


More information online: ilmt.co/PL/LxnE 64064pr@reply-direct.com


Optimising high-throughput tube marking


AFYS3G, a leader in laboratory automation solutions, has introduced its latest innovation, the Information Marking System Lambda768. This state-of-the-art laser marker is designed to streamline sample processing in laboratories by delivering exceptional speed, flexibility, and ease of use, making it an ideal solution for high-throughput tube marking.


The Lambda768 laser marker can handle up to 8 ANSI/SLAS racks at once, including 96-well, 48-well, and 24-well formats. Its ability to process mixed formats in a single run makes it incredibly versatile for labs working with various tube types. With a marking capacity of up to 500 tubes per hour, the Lambda768 optimises productivity by enabling quick, reliable marking for large sample volumes.


This advanced system is compatible with tubes from leading brands such as Micronic, Azenta, and LVL Technologies, ensuring smooth integration with existing lab supplies. A built-in turntable accelerates the picking and placing process, optimising speed even further.


Lambda768’s engraving capabilities are highly flexible, allowing high-resolution markings of images, logos, text, and batch IDs directly on the tube surface. The laser markings are durable, resistant to chemicals, abrasion, and extreme temperatures from -196°C to +100°C, ensuring clear legibility under even the harshest conditions.


Designed with usability in mind, the system features an intuitive touchscreen interface and user-friendly software, making it easy for laboratory staff to operate without extensive technical knowledge. An internal camera monitors the marking process in real-time, ensuring top-quality results. The Lambda768 can be operated via an internal PC or connected to external computers, LIMS systems, USB, or TCP/IP, offering flexibility for any lab setup. Its removable side panel allows easy integration into automated environments, reducing manual intervention and improving workflow efficiency.


The Lambda768 meets the growing demands of modern laboratories, offering a fast, flexible, and durable tube marking solution that ensures high-quality, precise results every time. Whether for research or high-volume sample processing, the Lambda768 delivers reliability and performance to enhance lab productivity.


More information online: ilmt.co/PL/kB9j 64068pr@reply-direct.com


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