AI and Machine Learning as Transformative BioTools

Nearly a century ago, Alexander Fleming discovered a penicillin-producing mold. Over the following decades, various microbes were used to make a range of therapeutics, from insulin to vaccines. Although gene-editing and other techniques can improve the production of microbe-based biologics, artificial intelligence (AI) could push these drugs even further.

“Artificial intelligence and machine learning play a crucial role as transformative tools in pharmaceutical research and microbial engineering,” according to Ayaz Belkozhayev, PhD, associate professor in the department of chemical and biochemical engineering at Satbayev University in Kazakhstan, and his colleagues. “These technologies enable the analysis of large datasets, the optimization of metabolic pathways, and the development of predictive models.”

Plus, Belkozhayev’s team points out that AI-based technologies can be used to develop efficient microbes that provide sustainable production of biotherapeutics. These biotherapeutics include ones that battle largely drug-resistant microbes, such as Acinetobacter baumannii, which can infect a person’s blood, lungs, wounds, and more.

AI-based tools could also be applied to microbes that produce lipophilic compounds, such as modified antibodies or peptides. Nonetheless, Zhang Dawei, PhD, an investigator in synthetic biology and microbial manufacturing engineering at the Tianjin Institute of Industrial Biotechnology in China, and his colleagues explain that lipophilic compounds can accumulate in cell membranes during fermentation, which can decrease production or even kill the cells producing the biotherapeutic.

To address this membrane-capturing problem, scientists explore what Dawei’s group called “membrane engineering techniques to construct highly flexible cell membranes … to break through the upper limit of lipophilic compound production.” AI could play a key role in this process. As Dawei’s group notes: “With the continuous advancement of artificial intelligence technology in the field of biomedicine, computer-assisted scientific research will provide a more comprehensive blueprint for the construction process of highly flexible cell membranes.”

Nonetheless, AI alone will not make better microbes for producing biologics. As Belkozhayev’s team emphasizes, “Innovations in genetic engineering, synthetic biology, adaptive evolution, [machine learning], and high-throughput screening have led to substantial progress in optimizing microorganisms for the efficient production of complex biological and chemical compounds.”

So, as is often the case, no one thing is the solution to all of the challenges in making biotherapeutics from microbes. Still, AI will probably enhance this area of bioprocessing.