Modern biotechnology has the tools to edit genes and design drugs, yet thousands of rare diseases remain untreated. According to executives at Insilico Medicine and GenEditBio, the missing ingredient took years to find enough smart people to keep working. AI is said to be becoming a force multiplier, enabling scientists to solve problems that industry has long left unchanged.
Speaking at Web Summit Qatar this week, Insilico President Alex Aliper outlined his company’s goal of developing “pharmaceutical superintelligence.” Insilico recently launched its “MMAI Gym” which aims to train general large language models such as ChatGPT and Gemini to perform as well as specialized models.
The goal is to create a multimodal, multitasking 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 address the shortage of manpower and talent in this space, because there are still thousands of diseases without treatment, without treatment options, and there are thousands of rare disorders that are neglected,” Aliper told TechCrunch. “So we need more intelligent systems to deal with this problem.”
The Insilico platform takes biological, chemical and clinical data and generates hypotheses about disease targets and candidate molecules. By automating steps that once required hordes of chemists and biologists, Insilico says it can explore vast design spaces, nominate high-quality therapeutic candidates, and even transform the use of existing drugs—all at dramatically reduced cost and time.
For example, the company recently used its artificial intelligence models to identify whether existing drugs could be repurposed to treat ALS, a rare neurological disorder.
But the bottleneck of the work does not end with the discovery of the drug. While AI can identify promising targets or therapies, many diseases require interventions at a more fundamental biological level.
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GenEditBio is part of the “second wave” of CRISPR gene editing, in which the process is moving from editing cells outside the body (ex vivo) to precise delivery inside the body (in vivo). The company’s goal is to make gene editing a one-time injection directly into the affected tissue.
“We developed our own ePDV, or engineered protein delivery vehicle, and it’s a virus-like particle,” GenEditBio co-founder and CEO Tian Zhu told TechCrunch. “We learn from nature and use AI machine learning methods to mine natural resources and find out which types of viruses have an affinity for certain tissue types.”
The “natural resource” Zhu refers to is GenEditBio’s massive library of thousands of unique, non-viral, non-lipid polymer nanoparticles—essentially 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 (such as the eye, liver or nervous system). The AI then predicts which improvements in the chemistry of the delivery vehicle will help it carry the cargo without triggering an immune response.
GenEditBio tests its ePDV in vivo in wet labs, and the results are fed back into the AI to improve its predictive accuracy for the next round.
Efficient, tissue-specific delivery is a prerequisite for in vivo gene editing, Zhu says. She says her company’s approach lowers the cost of goods and standardizes a process that has historically been difficult to scale.
“It’s like getting an over-the-counter drug (that works) for more patients, making drugs more accessible and affordable for patients around the world,” Zhu said.
Her company recently received FDA approval to begin trials of a CRISPR therapy for corneal dystrophy.
Battling a persistent data problem
As with many AI-driven systems, advances in biotechnology eventually run into a data problem. Modeling the edge cases of human biology requires far more high-quality data than scientists can currently obtain.
“We still need more ground truth data coming from patients,” Aliper said. “The corpus of data is heavily biased towards the Western world where it’s generated. I think we need to make more of a local effort to have a more balanced set of original data or ground truth data so that our models can better deal with that as well.”
Aliper said Insilico’s automated labs generate multi-layered biological data from large-scale disease samples, without human intervention, which they then feed into their AI-driven discovery platform.
Zhu says the data AI needs already exists in the human body, shaped by thousands of years of evolution. Only a small part of the DNA directly “codes” proteins, while the rest acts more like a guide for how the genes behave. This information has historically been difficult for humans to interpret, but is increasingly accessible to AI models, including recent efforts such as Google DeepMind’s AlphaGenome.
GenEditBio uses a similar approach in the lab, testing thousands of nanoparticles in parallel rather than one at a time. The resulting datasets, which Zhu calls “gold for AI systems,” are used to train her models and, increasingly, to support collaborations with external partners.
One of the next big efforts, according to Aliper, will be to build digital twins of people to conduct virtual clinical trials, a process he says is “still in its infancy.”
“We’re at a plateau of about 50 FDA-approved drugs each year, and we need to see growth,” Aliper said. “There is an increase in chronic disorders as we age as a global population. . . . I hope that in 10 to 20 years we will have more therapeutic options for personalized patient care.”