Artificial intelligence (AI) is expected to significantly contribute to the biotechnology field, by enhancing research, drug discovery, diagnostics and personalized medicine. The global biotech industry is projected to reach $465.9 billion USD by 2024 according to Global Biotechnology Industry Outlook 2024 – and AI’s transformation of the biotech industry should factor into this total. The following article will focus on how AI is changing the way scientists and researchers perform experiments and manufacture biotech products.
AI support for LIMS and ELN
One of the basic software components of any laboratory or company performing experiments is Laboratory Information Managements Systems (LIMSs) and Electronic Laboratory Notebooks (ELNs). Designed to store and track information about samples, results, while documenting processes, they make life easier for anybody performing wet lab work. Sure enough, those systems can be upgraded with AI.
A leading company in this space, Benchling, developed a few features that are now available for testing. By implementing Large Language Models (LLMs), they enable users to prompt the system with natural language phrases. As well, AI assists in creating search and filter parameters to deliver enhanced search results. Likewise, AI can help prepare and automate the generation of reports. The same company used Bayesian Optimization (a sequential optimization strategy for black-box functions that assumes no functional form) and classical machine learning techniques (ML) to offer built-in bioprocess and strain optimization. Furthermore, AlphaFold, an AI system developed by Google DeepMind, supports 3D model creation and visualization, a solution that might be useful for anybody engaged with protein experiments and optimization. Other companies, such as Alchemy or CloudLIMS, point out many benefits of using AI in ELN and LIMS, including increased efficiency, optimal resource allocation, future proofing and acceleration of regulatory compliance.
The importance of predictive maintenance
Ensuring repair readiness is crucial to maintaining stable and uninterrupted operations in industries that depend on them. A factory shutdown, even for a short time, can cause significant financial harm. In fact, asset management systems have had predictive maintenance, part or machine failures, built-in for a very long time.
For people working in biotechnology production plants, it is one of the crucial systems to have in place. Some well-known names and products in this area include IBM Maximo or AsahiKASEI Bioprocess. As expected, AI is being introduced to generate machine and maintainer behavior models that help direct attention and expertise to the most important places. Such a solution can be found in Siemens’ portfolio and goes by the name of Senseye Predictive Maintenance.
How AI propels biotech and life sciences breakthroughs
Drug discovery
Artificial intelligence is revolutionizing drug discovery by rapidly analyzing vast molecular datasets to identify promising candidates. This accelerates the process, lowers costs, and increases the success rate of new therapies by early prediction of their efficacy and potential side effects.
Big Data
The vast amount of “omics” data, including genomics and proteomics, in biotechnology is leveraged by AI. Big Data analytics reveal new biological insights, identify disease biomarkers, and enable the development of personalized medicine through the analysis of complex patient datasets.
CRISPR
AI improves CRISPR gene-editing technology by enhancing the precision and efficiency of guide RNA design, thereby minimizing unintended off-target effects. This collaboration accelerates the development of safer and more effective gene therapies, as well as advanced genetic research, expanding the potential of biotechnology.
Vaccine development
Artificial intelligence plays a crucial role in accelerating vaccine development by quickly identifying potential antigens and modeling their effectiveness. AI algorithms optimize clinical trial designs and can predict manufacturing challenges, enabling quicker responses to emerging infectious diseases and enhancing global health security.
Digital Twin for biopharma processes
A natural growth from modeling single assets or maintenance behavior is to create a virtual representation of an object or whole system in its environment. This virtual representation, called a digital twin, can be used to simulate process and equipment behavior under different conditions. Furthermore, by connecting sensors from the modelled object and feeding it into the digital twin, the model is corrected and provides more accurate results. Lastly, the system can be used to monitor and control the process it operates on if the connection is bidirectional.
Tony Manzano and William Withford, in BioProcess International magazine, have provided an excellent rationale for using digital twins and AI in bioprocessing 4.0. All the great names in pharmaceutical manufacturing are already implementing this trend and advocating the benefits of AI implementation. Gareth John Macdonald at GenEngNews lists Lonza, Sartorius, Cytiva and Pfizer among the leaders in cutting costs and production time thanks to digital transformations they’ve introduced in their manufacturing plants. This approach is available not only for the most prominent companies. You can enlist experts in biotech software development who are able to implement a complete digital twin that will suit your needs.
How does agricultural biotechnology benefit from AI?
Farming can improve its management systems, similar to other parts of the bio-industry, with the right software tools. Farmers are now equipped with monitoring, analytics and data-driven decision-making. Innovative solutions provide optimization of areas such as logistics and plant growth. The expected benefits are increased profitability, better livestock health and boosted overall planning. Both academia (e.g., Fraunhofer Institute in Germany), and private sector companies (such as Intellis) are offering their hand to this branch of industry transformation.
The ways software and artificial intelligence can contribute to monitoring have been extensively explored. Modeling atmospheric conditions in an advanced manner can lead to better automated irrigation systems. Computer vision helps with leaf disease detection and weed control. On top of that, using unmanned air vehicles (UAVs) helps lead real-time monitoring tasks over large areas. Pedro Dias offered an excellent summary of the above use cases in the Nilg.ai blog, which is an insightful reading companion piece to the New Biotechnology publication of Andreas Holzinger and his team, which broadly explores the topic of AI for life: Trends in artificial intelligence for biotechnology.
Clinical Applications: AI and Software Development
Muscular dystrophy
AI-powered software accelerates the diagnosis of muscular dystrophy by analyzing medical images and genomic data with high precision. Digital platforms and wearable sensors help monitor disease progression, manage physical therapy, and collect data for clinical trials, enabling more personalized patient care and supporting research for new treatments.
Cystic fibrosis
For cystic fibrosis, AI algorithms analyze patient data to predict pulmonary exacerbations and personalize treatment plans. Custom software applications help patients manage complex daily medication schedules, monitor lung function through connected devices, and facilitate remote consultations, improving quality of life and enabling proactive clinical intervention.
Mental illness / mental health illnesses
AI-driven applications are transforming mental healthcare by providing accessible support through chatbots and virtual therapists. Software uses smartphone and wearable data to monitor mood and behavior, offering early intervention alerts. AI also helps clinicians diagnose conditions by analyzing patient language, audio, and brain imaging data.
SARS-CoV-2
During the pandemic, AI was critical for clinical applications. Machine learning models analyzed chest CT scans for rapid diagnosis and predicted disease severity to manage hospital resources. Software enabled large-scale contact tracing, streamlined vaccine distribution logistics, and powered telehealth platforms to provide care while minimizing viral transmission.
The influence AI has on forest biotechnology
Dr. Holzinger’s team also points out that AI can be beneficial with forest biotechnology and obtaining sustainable production of wood in the increasing global need for it. AI can support various tasks including predictive modeling for planting and productivity, detection of diseases and pests, forest health monitoring and wildfire detection and resources and inventory management. A compelling example of work in this direction is a mathematical model working with satellite imagery to estimate timber volume loss due to storm damage developed by Austrian researchers.
On the commercial side, a few European companies offer innovative AI-powered solutions for the timber industry. The Collective Crunch provides a platform that enables sustainable forestry at scale, by monitoring forest health, collecting and processing climate and geo data and predicting forest inventory. Thai company STelligence offers support in making sure business buys timber from sustainable sources compliant with deforestation-free commitments. US-based Overstory offers data-driven vegetation management to reduce the number of power outages and electric grid-caused wildfires. For more examples on AI transformation in the timber industry refer to AchiExpo Magazine.
AI and the search for a perfect beer
In their research for the perfect pint, a group from Belgium (a country famous for excellent beers in hundreds of flavors), employed gradient boosting to analyze 250 different beers and data from 180,000 reviews. Their scientific publication Predicting and Improving Complex Beer Flavor Through Machine Learning identified unexpected compounds as drivers of flavor and appreciation. The work serves as an example that machine learning and artificial intelligence can decode food chemistry and be potentially used to create novel, tailored foods with better consumer appreciation.
You may be surprised to learn that the scientific work mentioned above is being published almost a year after an Australian brewery (Modus Brewing) announced that they produced a beer whose recipe was designed by AI. Sadly, the Neural Network East Coast IPA turned out to be a limited-edition production. This year, what could have been considered a joke a year ago became scientific research and may lead to better products in the future.
Ethical and legal dimensions of AI in biotechnology
Flawed or unlawfully sourced data can sink any AI project: bias skews outputs, privacy evaporates, and regulators intervene. Labs must apply data hygiene with the same rigor as sterility controls. Each record should clear three gates: provenance proof, bias screening, and irreversible de‑identification. Under the EU AI Act, GDPR, and FDA change‑control rules, teams must document, audit, and monitor or risk blocked approvals and liability claims.
Dataset checklist
- Provenance log confirming lawful origin and consent
- Bias audit across age, sex, ancestry
- De‑identification score with re‑ID risk threshold
- End‑to‑end encryption
- Retention policy tied to legal timelines
Ethical guardrails and accountability
Neural nets cannot remain black boxes in life sciences. Model cards outlining limits should accompany every algorithm, and clinicians need override rights to meet informed‑consent norms. Patent law still requires a human inventor even when AI supplies the idea, so companies mix molecule patents with trade‑secret protection of training workflows.
Trustworthy biotech AI rests on three pillars: transparent logic, shared responsibility between developers and clinicians, and post‑market surveillance that detects drift before patients feel the impact.
AI specialists will lead the way for biotechnology
AI has obvious potential to improve the biotechnology industry, and such solutions are gaining traction worldwide. This article aimed to direct readers’ attention to the less intuitive, yet potentially valuable, ideas. Even the oldest branches of bio-production or products can gain a new cutting-edge advantage thanks to AI. Sometimes the most challenging questions are not about which algorithm to use, but what task to employ it to. Therefore, a collaboration between domain experts and AI specialists is the best way forward. This partnership is a proven way to quickly apply state-of-the-art solutions to your company’s goals. The biotechnology industry’s transformation with AI has only just started, and the right team of AI experts can help organizations advance this trend. Use this form to contact one of our experts and accelerate your business.
Discover our case studies on biotechnology and learn how we assisted various companies in leveraging tech in life sciences:
Building a comprehensive platform for medical review officers
Implementing AI for research aggregation and advanced analytics to track animal health
Sources
Benchling: https://www.benchling.com/ai
Alchemy: https://www.alchemy.cloud/why
CloudLIMS: https://cloudlims.com/ai-ml-lims-for-environmental-laboratories-reshaping-lab-future/
IBM Maximo: https://www.ibm.com/products/maximo/predictive-maintenance
AsahiKASEI Bioprocess: https://fluidmgmt.ak-bio.com/wp-content/uploads/2023/08/The-Column-2.7-Predictive-Maintenance.pdf
Siemens Senseye Predictive Maintenance: https://www.siemens.com/global/en/products/services/digital-enterprise-services/analytics-artificial-intelligence-services/predictive-services/senseye-predictive-maintenance.html
AI-Enabled Digital Twins in Biopharmaceutical Manufacturing https://www.bioprocessintl.com/sponsored-content/ai-enabled-digital-twins-in-biopharmaceutical-manufacturing
Bioprocessing 4.0: https://www.genengnews.com/topics/bioprocessing/biopharma-taps-bioprocessing-4-0-benefits-start-flowing/
Digital Twin for biopharma process: https://a4bee.com/case/creating-digital-twin-to-execute-simulations-of-biopharma-processes/
Fraunhofer Institute COGNAC: https://www.ipa.fraunhofer.de/en/reference_projects/cognac.html
Intellias Agriculture solutions: https://intellias.com/agriculture/
“Crop monitoring & AI: The future of agriculture”: https://nilg.ai/202105/crop-monitoring-ai-the-future-of-agriculture/
“AI for life: Trends in artificial intelligence for biotechnology”: https://www.sciencedirect.com/science/article/pii/S1871678423000031
“Estimating timber volume loss due to storm damage in Carinthia, Austria, using ALS/TLS and spatial regression models: https://www.sciencedirect.com/science/article/pii/S0378112721008057/pdfft?md5=5f6882e87e2bbec41db57dfc91cc9498&pid=1-s2.0-S0378112721008057-main.pdf
Overstory Vegetation Intelligence: https://www.overstory.com
Satelligence Sustainability Monitoring: https://satelligence.com
CollectiveCrunch AI Platform: https://www.collectivecrunch.com/product/
AI in Timber Industry, ArchiExpo: https://emag.archiexpo.com/ai-in-the-timber-industry-sustainable-construction-and-reducing-illegal-logging/
AI-Based Software Tools for Beer Brewing Monitoring and Control:
“Predicting and improving complex beer flavor through machine learning”: https://www.nature.com/articles/s41467-024-46346-0
Neural Network East Coast IPA: https://www.beerandbrewer.com/modus-brewing-release-ai-designed-beer/
About the authorJacek Szmatka
Head of Life Sciences
An open-minded leader with over 20 years’ experience in the IT world, Jacek’s career has seen him evolve from a computer science graduate to software engineer to a co-founder and CTO of a tech start-up. Before joining Software Mind, Jacek was part of a team that developed a bioinformatics company and served as an executive board member. In his current role as Head of Life Sciences, Jacek helps leading life sciences companies design and build innovative solutions. A true believer in the transformative power technology can have on our lives, Jacek maintains a keen interest in R & D, in particular with solutions that involve AI, IoT, life science and cloud technologies.
