How AI is helping solve the labor issue in treating rare diseases Rebecca Bellan 6:29 AM PST · February 6, 2026 Modern biotech has the tools to edit genes and design drugs, yet thousands of rare diseases remain untreated. According to executives from Insilico Medicine and GenEditBio, the missing ingredient for years has been finding enough smart people to continue the work. AI, they say, is becoming the force multiplier that lets scientists take on problems the industry has long left untouched.
Speaking this week at Web Summit Qatar, Insilico’s president, Alex Aliper, laid out his company’s aim to develop “pharmaceutical superintelligence.” Insilico recently launched its “MMAI Gym ” that aims to train generalist large language models, like ChatGPT and Gemini, to perform as well as specialist models.
The goal is to build a multimodal, multitask model that, Aliper says, can solve many different drug discovery tasks simultaneously with superhuman accuracy.
“We really need this technology to increase the productivity of our pharmaceutical industry and tackle the shortage of labor and talent in that space, because there are still thousands of diseases without a cure, without any treatment options, and there are thousands of rare disorders which are neglected,” Aliper said in an interview with TechCrunch. “So we need more intelligent systems to tackle that problem.”
Insilico’s platform ingests biological, chemical, and clinical data to generate hypotheses about disease targets and candidate molecules. By automating steps that once required legions of chemists and biologists, Insilico says it can sift through vast design spaces, nominate high-quality therapeutic candidates, and even repurpose existing drugs — all at dramatically reduced cost and time.
For example, the company recently used its AI models to identify whether existing drugs could be repurposed to treat ALS, a rare neurological disorder.
But the labor bottleneck doesn’t end at drug discovery. Even when AI can identify promising targets or therapies, many diseases require interventions at a more fundamental biological level.
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“We have developed a proprietary ePDV, or engineered protein delivery vehicle, and it’s a virus-like particle,” GenEditBio’s co-founder and CEO, Tian Zhu, told TechCrunch. “We learn from nature and use AI machine learning methods to mine natural resources and find which kinds of viruses have an affinity to certain types of tissues.”
The “natural resources” Zhu is referring to is GenEditBio’s massive library of thousands of unique, nonviral, nonlipid polymer nanoparticles — essentially delivery vehicles designed to safely transport gene-editing tools into specific cells.
The company says its NanoGalaxy platform uses AI to analyze data and identify how chemical structures correlate with specific tissue targets (like the eye, liver, or nervous system). The AI then predicts which tweaks to a delivery vehicle’s chemistry will help it carry a payload without triggering an immune response.
GenEditBio tests its ePDVs in vivo in wet labs, and the results are fed back into the AI to refine its predictive accuracy for the next round.
Efficient, tissue-specific delivery is a prerequisite for in vivo gene editing, says Zhu. She argues that her company’s approach reduces the cost of goods and standardizes a process that has historically been difficult to scale.
“It’s like getting an off-the-shelf drug [that works] for multiple patients, which makes the drugs more affordable and accessible to patients globally,” Zhu said.
Her company recently received FDA approval to begin trials of CRISPR therapy for corneal dystrophy.
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