Transforming Disease Detection with AI-Driven Biological Insights

AI unlocking biological complexity: From data to decision

The challenge of interpreting vast amounts of biological data, which has long slowed progress in disease detection and drug discovery, is precisely where AI offers a valuable solution.

For example, liquid biopsy, which analyzes DNA fragments circulating in the blood, exemplifies AI’s power to break new ground. Unlike invasive tissue biopsies, liquid biopsy offers a minimally invasive window into the body’s molecular makeup.

However, signals in blood can be extremely subtle, especially for chronic diseases like liver conditions, which have eluded detection with older methods. Transformer-based AI models adapted to biological data can analyze millions of molecular interactions simultaneously, uncovering faint patterns and signatures that traditional methods miss, enabling early detection and personalized diagnostics that can dramatically improve outcomes.

Hepta, a liquid biopsy company developed by experts from Illumina (NASDAQ:ILMN) and GRAIL, Inc. (NASDAQ:GRAL), has created an AI platform that analyzes epigenetic patterns in circulating cell-free DNA.

The company recently came out of stealth mode with US$6.7 million in seed funding led by Felicis Ventures and Illumina Ventures, among others. Its technology is designed to replace invasive biopsies with simple blood draws, showing strong early clinical results for detecting liver disease.

CEO Hamed Amini described how Hepta’s platform is uniquely designed from the ground up to deliver a specialized approach that sets it apart from earlier AI tools used in cancer research, opening the door to new possibilities for broad applications.

“I think in the not-distant future, we’re going to be at a place where generating ample genomic data is truly not going to be a cost barrier anymore,” he said. “Once you get there, I envision a super comprehensive central assay that captures all this epigenetic signal from a blood sample (to determine patient eligibility for certain treatments). You can expand this to oncology and other chronic diseases down the line, hopefully.’

AI accelerates drug discovery and personalizes cancer care

AI is also drastically transforming cancer care by accelerating drug discovery and development, with the potential to revolutionize medicine, according to an editorial in the Lancet Oncology. Author Abhishek Mehta observes that many academic cancer centers are collaborating with private companies to use AI for optimizing drug development, trials and analytics.

For example, the cancer drug BBO-10203 was developed by researchers at Lawrence Livermore National Laboratory and Frederick National Laboratory for Cancer Research in collaboration with private biotech company BridgeBio Oncology Therapeutics. Developers used advanced computing and AI to go from conception to human trials in just six years. This is a stark improvement to the 10 to 15-year timeline of the traditional drug development process.

Other key innovators include Rakovina Therapeutics (TSXV:RKV), a Canadian biotech company, which is using its AI platforms, Deep-Docking and Enki, to help discover drugs that target the DNA damage repair process in cancer cells.

One of its main programs is a therapy that blocks a key protein that cancer cells need to survive. Rakovina has found promising candidates and is working with top cancer research centers to move these treatments toward human trials.

Recently, it has partnered with a biotech company specializing in advanced lipid nanoparticle technology designed with AI assistance to develop AI-discovered cancer therapies. The company has also expanded access for US investors through new trading eligibility.

Beyond optimizing drug candidates and delivery mechanisms, AI is also being deployed to develop targeted therapeutic strategies.

“The next improvement in human life and survival comes from the next platform shift, and we really believe that metabolism is that, and this trial would allow us to really open that door for people in their minds,” Parikh explained, adding that the complexity of metabolic function necessitates the need for machine learning. “There are no approaches around metabolism and cancer that can thrive and survive and be reproducible without leveraging machine learning.”

Personalized, data-driven healthcare’s expanding frontier

Looking ahead, AI is reshaping drug development pipelines, with techniques like DeepDR and SNF-CVAE expected to enhance drug discovery and repurposing, speeding up clinical timelines.

For investors, the economic implications of such efficiency gains are profound: faster approvals and lower development costs can significantly increase returns while reducing risk.

Not only will AI tools help pharmaceutical companies select promising candidates faster and design smarter trials, but industry insiders maintain that they can eventually help physicians personalize therapies to patient-specific profiles.

In Faeth’s case, its AI-driven MetabOS platform reduces the data related to cancer metabolism to a smaller, more tractable set of potential targets. Then, CRISPR gene-editing technology allows further experimental validation and refinement to identify the most promising therapeutic candidates with high precision.

“There’s a cohort of patients .. .that are really benefiting,” Parikh said of the DICE trial. “And so we’re going to figure out who those patients are, and then make sure physicians are getting those patients on it early so they can derive the maximum benefit.”

However, widespread adoption faces hurdles, including regulatory pathways and data quality standards; still, growing investor interest and strategic partnerships indicate strong momentum in overcoming these barriers. As Parikh said, “If the data … (are good), more capital will come.”

For example, Danish medical AI company Corti is increasingly finding traction by offering healthcare institutions “AI infrastructure” designed specifically for medical use cases

Governments are also investing heavily in AI for disease research, exemplified by the US$500 billion US Stargate Project, which includes funding allocated to AI-driven biomedical research and infrastructure development; and the UK’s £19 million PharosAI initiative supporting AI-powered cancer research and clinical innovation.

The bottom line

AI-driven platforms are on the frontline of healthcare innovation.

For investors with an eye toward the future, this is an opportunity to support transformative science while participating in a market with tremendous growth potential. This is not just about technology; it’s about changing how medicine is practiced and ultimately, how lives are improved and saved.