AI in biotech is rapidly transforming the industry, whether it is from clinical testing to immune system mapping. There is a new era of speed, precision and innovation pushing biotech forward. Across the globe, biotech leaders and researchers are using generative models to design new therapies, improving accuracy in diagnostics and drug evaluation. Traditional approaches have long struggled with intensive, time-consuming experimentation; AI now allows faster timelines. From decoding the human immune system to reading DNA in minutes, these breakthroughs mark a turning point in biotech evolution.
The advent of AI for peptide design: An emerging field – Labiotech
Traditionally, biochemical and medicinal chemistry approaches have been used for peptide drug discovery; however, these have relied on experimental and empirical methods, which often entail a significant amount of time, resources, and expertise – particularly when you consider that one of the primary challenges surrounding traditional peptide drug discovery is the vast chemical space that needs to be explored, as the number of possible peptide sequences is exponentially large, making it impractical to test all potential candidates experimentally.
This is where AI comes in extremely handy, as the idea of incorporating the technology into any area of biotech is to speed up certain processes. AI-based peptide design generally involves the use of algorithms that can generate and evaluate large numbers of peptide sequences based on desired properties, such as target affinity, selectivity, and bioavailability. Ultimately, these algorithms can explore large chemical spaces more comprehensively and efficiently than traditional methods.
Between hype and hope: Seattle biotech leaders size up AI’s real impact on drug development – Geekwire
“What was science fiction 15 years ago is now reality,” said Erik Procko, chief scientist for Cyrus Biotechnology. “So yes, there’s been hype. But there’s still enormous potential, and sometimes the progress that is being made is just dizzying. Lazarovits noted that while AI can generate exciting leads and information, it doesn’t mean much until it has been tested in actual experiments with cells and organisms.
AI is great for engineering new therapies, but the most costly, laborious part of the drug development process is seeing how they work in patients. “The most impactful place for AI to really change the game for drug development would be to make smaller, better powered clinical studies,” said Lajoie. The way to do that, he added, was coming up with better drug candidates that perform multiple functions. “AI is fantastic these days for predicting, say, a protein structure, but for creating new drugs it hasn’t yet found its killer app,” said Procko. “What is it that AI is letting us do now to make new drugs that were simply impossible to make before? How is it being a game changer?”
Generative AI models build new antibiotics starting from a single atom – FierceBiotech
Researchers have tapped into the power of generative artificial intelligence to aid them in the fight against one of humanity’s most pernicious foes: antibiotic-resistant bacteria. Using a model trained on a library of about 40,000 chemicals, scientists were able to build never-before-seen antibiotics that killed two of the most notorious multidrug-resistant bacteria on earth.
Two lead compounds, NG1 and DN1, both were able to eliminate multidrug-resistant gonorrhea, while DN1 could also kill methicillin-resistant Staphylococcus aureus (MRSA). MRSA is “probably the most famous of the resistant pathogens,” James Collins, Ph.D., a synthetic biologist at the Broad Institute of MIT and Harvard and leader of the new study, told Fierce Biotech.
AI-designed modifications to the immune system help target cancer cells — Biotechniques
Precision personalized cancer treatment on a larger scale has come a step closer after researchers from the Technical University of Denmark (DTU; Kongens Lyngby, Denmark) developed an AI platform that can tailor protein components and equip a patient’s immune cells to fight cancer. The new method shows for the first time that it is possible to computer-design special binder molecules that can guide the patient’s immune cells to bind to the pMHC molecules of the cancer cells.
Our AI platform designs molecular structures against cancer cells, and the platform does it at an incredible speed, so we can have a new promising molecule within 4-6 weeks,” reported DTU associate professor and co-author of the study Timothy P. Jenkins.
AI Outperforms People in Scoring Melanoma Tumor-Infiltrating Immune Cells — Yale School of Medicine
New research from Yale confirms that artificial intelligence (AI)-based scoring of melanoma tumor-infiltrating immune cells called lymphocytes significantly outperforms traditional pathologist eyeballing. The study, published in JAMA Network Open, found open-source AI tools offered a more standardized and reproducible method for assessment, underscoring the potential for AI to enhance clinical pathology workflows.
“Our findings suggest that an AI-driven lymphocyte quantification tool may provide consistent, reliable assessments with a strong potential for clinical use, offering a robust alternative to traditional methods,” says lead author Thazin Nwe Aung, PhD, associate research scientist in pathology at Yale School of Medicine (YSM).
MassBio and Google empower biotech workers with AI for Drug Discovery Training – MassBio News
Artificial intelligence is no longer a distant promise; it’s already reshaping the way we discover, test, and deliver new medicines. From predicting protein structures to optimizing clinical trial design, AI is accelerating timelines, reducing costs, and uncovering insights that would be impossible to see with traditional methods alone.
MassBio’s Vision 2030 report identified Techbio, the convergence of life sciences and advanced technology, as a critical growth engine for Massachusetts’s innovation ecosystem. Training programs like this one are designed to ensure our community stays ahead of the curve, building a workforce fluent in biology, artificial intelligence, machine learning, and data science.
Lilly to give biotech startups access to AI tools – BioPharma Dive
Eli Lilly will give small biotechnology companies a chance to use artificial intelligence models trained on years of the pharmaceutical company’s research, launching Tuesday a new platform its says could help young startups a leg up in discovering new drug molecules.
Called TuneLab, the platform incorporates data Lilly’s obtained developing “hundreds of thousands of unique molecules.” Biotechs can access these datasets and the AI models trained on them using a distributed system designed to protect proprietary information. In return, Lilly can refine its AI models and use data contributed by participating companies.
“These models have the potential to be a game-changer by giving researchers an elegant and powerful way to zero in on drug-like chemical structures at the earliest stages,” said Philip Tagari, Insitro’s chief scientific officer, in a statement.
Researchers develop AI system to decode the human immune system – News Medical & Life Sciences
One key branch of research in immunology involves the identification of immune system components and ascertaining their function. Doing this through manual observation would be impossible due to the time it would take, and some automated tools exist, but have limitations around accuracy, consistency or flexibility. To this end, a team of researchers led by Professor Tatsuhiko Tsunoda from the University of Tokyo’s Department of Biological Sciences rose to the challenge and developed a system to boost immunology research.
scHDeepInsight is primarily a research tool rather than a full diagnostic system, partly due to its infancy, but mainly as the model is only trained on healthy cells. By applying it to patients’ samples, researchers can see where they deviate from a healthy baseline. Such deviations may provide clues for further study, but medical interpretation requires additional validation. So this development will aid in fundamental research throughout the field of immunology, but it might take time before descendants of scHDeepInsight find their way into diagnostic systems.
AI can decode digital data stored in DNA in minutes instead of days – New Scientist
Artificial intelligence can read data stored in DNA strands within 10 minutes rather than the days required for previous methods, bringing DNA storage closer to practical use in computing.
Bar-Lev and her colleagues developed an AI-powered method called DNAformer that can quickly and accurately decode jumbled DNA sequences. The system includes a deep learning AI model trained to reconstruct DNA sequences, a separate computer algorithm that identifies and corrects errors and a third decoding algorithm that converts everything back into digital data while fixing any remaining mistakes.
AI tool offers deep insight into the immune system – Medical Xpress
For the first time, researchers, including those from the University of Tokyo, built a software tool which leverages artificial intelligence to not only offer a more consistent analysis of these cells at speed but also categorizes them and aims to spot novel patterns people have not yet seen.
One key branch of research in immunology involves the identification of immune system components and ascertaining their function. Doing this through manual observation would be impossible due to the time it would take, and some automated tools exist, but have limitations around accuracy, consistency or flexibility.
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***News roundup guest post from FischTank PR interns Abby Collins and Nana Duah***
