Since the term ‘molecular disease’ was first coined in 1949,1 the field of molecular diagnostics has been developing rapidly. Molecular diagnostic testing techniques such as polymerase chain reaction (PCR), isothermal amplification, microarray technology and high-throughput sequencing have been increasingly used to detect infectious disease pathogens.2 Now, artificial intelligence (AI) and machine learning (ML) are poised to revolutionize the molecular diagnostics sector. Let’s explore the applications of AI in molecular diagnostics and other areas of medicine.
Molecular diagnostics involves analyzing DNA or RNA sequences to identify specific markers or signals that may indicate the presence or risk of certain diseases. For example, if a newborn baby is found to have the gene mutations associated with cystic fibrosis, the baby is likely to have the condition and can be treated at an early stage, leading to better outcomes. Additionally, molecular testing can reveal whether a patient has developed resistance to a particular medication, helping guide necessary adjustments to their treatment plan.3,3a
The impact of AI is being felt in many different areas of molecular diagnostics.
Protein engineering – using genetic technology to improve protein functions or create new ones – allows scientists to produce proteins with properties specifically beneficial for a range of applications, including ecology, food security, drug discovery and diagnostic medicine.4,5
AI models, including ML algorithms and deep learning (DL) frameworks, are currently being used to:5a
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“Leveraging accumulative protein databases, machine learning (ML) models, particularly those based on natural language processing (NLP), have considerably expedited protein engineering. Moreover, advances in topological data analysis (TDA) and artificial intelligence-based protein structure prediction, such as AlphaFold2, have made more powerful structure-based ML-assisted protein engineering strategies possible.” — Qiu, Department of Mathematics, Michigan State University, USA, 20235 |
But protein engineering also improves molecular diagnostics speed and accuracy:3b
AI is transforming protein engineering by enabling rapid, precise design of enzymes like polymerases, which are critical for molecular diagnostics. In an era where even slight variations can affect test outcomes and treatment decisions, AI helps identify and optimize polymerase variants that perform reliably across diverse testing conditions. Medix Biochemica’s polymerase library exemplifies this innovation, offering a curated selection of enzyme mutants pre-screened for high performance, inhibitor resistance, and adaptability. Combined with expert buffer optimization and close collaboration with test developers, AI-driven protein engineering supports the creation of tailored, high-precision diagnostic tools that meet evolving healthcare demands.5a
Digital PCR (dPCR) is a highly precise method for measuring DNA or RNA. It’s a novel and rapidly growing area which will see more use as molecular diagnostics continue to improve. dPCR partitions a sample into thousands of tiny droplets or wells, each of which undergoes a PCR reaction, and determines whether each partition is positive or negative for the target DNA sequence. dPCR provides absolute quantification of DNA or RNA, in contrast to quantitative polymerase chain reaction (qPCR), which requires a standard curve for relative quantification. dPCR is also more sensitive than qPCR for detecting low concentrations of nucleic acids.3b,6
ML is being used to enhance dPCR by improving data interpretation and test optimization. The benefits of supporting digital PCR with AI include:7,8
Here are some examples of how AI diagnostics solutions are helping to diagnose diseases earlier and treat them more efficiently, for better patient outcomes.
Overall, research confirms that AI-enabled decision support systems, when used correctly, can improve patient safety by reducing errors and enhancing drug management.10
Molecular diagnostics requires the handling of complex data and samples so there are several common challenges facing the industry, which AI can help to address. Here are some examples of how AI streamlines molecular diagnostics processes in a laboratory setting:11
While ML and AI diagnostics have the potential to be transformative, there are still some barriers to their implementation, e.g. if AI is not properly trained – using representative and unbiased data – it may produce unreliable or inaccurate results, with disastrous consequences in the healthcare space.11
Implementing AI in a lab setting also introduces new vulnerabilities, as sensitive data becomes a target for cyberattacks. AI systems must be fully secure to ensure the protection of data and patient privacy.11
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Many people mistrust AI and believe it will lead to job losses. AI must be reframed as a tool that can enhance human capabilities instead of replacing them.11 |
More advanced AI technologies, including quantum AI (QAI), are being introduced into the research domain to accelerate the training process and improve rapid diagnostics models. Quantum computers have far more processing power than conventional computers, which could allow QAI algorithms to analyze enormous amounts of medical data in real-time, leading to more accurate and efficient diagnoses.12
In protein engineering (or enzyme engineering) specifically, ML is set to play an increasingly important role in the coming years. Generative ML models have the potential to unlock new enzymes with a diverse range of functions, evolvabilities and folds. Supervised ML also offers a unique opportunity to accelerate protein fitness optimization by more efficiently selecting which protein variants to synthesize and screen. ML can even suggest protein variants that would not usually be considered.13
Directed evolution is a powerful enzyme engineering technique used to improve and tailor the functions of enzymes by mimicking natural evolutionary processes in the lab. AI is also being incorporated into directed evolution to significantly enhance the process’s efficiency, precision and outcomes. What’s more, AI can predict beneficial mutations, identifying which amino acid changes are likely to improve the protein's performance.3a
These trends highlight the importance of adopting AI and precision enzyme engineering for future diagnostics.
Learn more about Medix Biochemica's MDx offerings and capabilities
As molecular diagnostics labs face increasingly large sample and data volumes, it makes sense to adopt new technologies like AI solutions to help automate processes and analyze huge amounts of data. Labs can improve the speed, efficiency and accuracy of their operations by harnessing AI-driven data analysis, workflow automation, quality control and more.
MedixMDx offers a comprehensive range of raw materials for molecular biology applications. Our range includes polymerases designed for end-point PCR, real-time PCR and isothermal applications, as well as lyo-ready and lyophilized polymerases. Thanks to our robust product offerings and unwavering commitment to quality, you can rely on us as a trusted partner to support you in developing high-quality, accurate molecular diagnostic assays.14
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