Jakarta, INTI - Modern biotechnology has the capability to edit genes and design new drugs, yet countless rare diseases still lack treatments. Executives from Insilico Medicine and GenEditBio point out that the limiting factor for years has been finding enough skilled personnel to continue this work. They argue that AI is emerging as a force multiplier, enabling scientists to tackle problems the industry has historically overlooked.

At Web Summit Qatar, Insilico’s president, Alex Aliper, outlined the company’s vision to develop “pharmaceutical superintelligence.” Recently, Insilico launched its “MMAI Gym”, designed to train generalist large language models, such as ChatGPT and Gemini, to perform at the level of specialist models.

The aim is to create a multimodal, multitask model that, according to Aliper, can handle multiple drug discovery tasks at once 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 processes biological, chemical, and clinical data to generate hypotheses on disease targets and candidate molecules. By automating tasks that once required large teams of chemists and biologists, Insilico claims it can navigate vast design spaces, propose high-quality therapeutic candidates, and even repurpose existing drugs, all at a fraction of the traditional cost and time.

For instance, the company recently applied its AI models to determine whether existing drugs could be repurposed to treat ALS, a rare neurological disorder.

However, the labor bottleneck extends beyond drug discovery. Even when AI identifies promising targets or therapies, many diseases still demand interventions at a fundamental biological level.

GenEditBio represents the “second wave” of CRISPR gene editing, shifting from manipulating cells outside the body (ex vivo) to delivering precise treatments directly inside the body (in vivo). The company aims to make gene editing a single injection directly into affected tissues.

“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 mentions refer to GenEditBio’s extensive library of thousands of unique, nonviral, nonlipid polymer nanoparticles, specialized delivery vehicles designed to safely transport gene-editing tools into targeted cells.

According to the company, its NanoGalaxy platform leverages AI to analyze chemical structure data and determine correlations with specific tissue targets, such as the eye, liver, or nervous system. The AI then predicts which chemical modifications will enable a delivery vehicle to carry its payload without triggering an immune reaction.

GenEditBio conducts in vivo testing of its ePDVs in wet labs, and the results are fed back into the AI to improve predictive accuracy for subsequent iterations.

Efficient, tissue-specific delivery is essential for in vivo gene editing, Zhu explains. She emphasizes that this approach reduces manufacturing costs and standardizes a process that has traditionally been hard 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.

Recently, the company received FDA approval to begin CRISPR therapy trials for corneal dystrophy.

Addressing the Ongoing Data Challenge 

As in many AI-powered systems, biotech progress ultimately encounters a data bottleneck. Modeling the rarer aspects of human biology demands far more high-quality data than is currently accessible to researchers.

“We still need more ground truth data coming from patients,” Aliper said. “The corpus of data is heavily biased over the Western world, where it is generated. I think we need to have more efforts locally, to have a more balanced set of original data, or ground truth data, so that our models will also be more capable of dealing with it.”

Aliper explained that Insilico’s automated labs produce multi-layer biological data from disease samples at scale without human intervention, which is then fed into its AI-driven drug discovery platform.

Zhu noted that the data AI requires already exists in the human body, shaped over millennia of evolution. While only a small portion of DNA directly encodes proteins, the remainder functions as an instruction manual guiding gene behavior. Historically difficult for humans to interpret, this information is increasingly accessible to AI models, including tools like Google DeepMind’s AlphaGenome.

GenEditBio uses a similar strategy in the lab, testing thousands of delivery nanoparticles simultaneously instead of individually. The resulting datasets, which Zhu refers to as “gold for AI systems,” are employed to train AI models and support collaborations with external partners.

One of the upcoming major initiatives, according to Aliper, is the development of digital human twins to conduct virtual clinical trials, a process he describes as “still in nascence.”

“We’re in a plateau of around 50 drugs approved by the FDA every year annually, and we need to see growth,” Aliper said. “There is a rise in chronic disorders because we are aging as a global population … My hope is in 10 to 20 years, we will have more therapeutic options for the personalized treatment of patients.”

Conclusion 

AI is rapidly transforming biotech by overcoming labor and data limitations, enabling faster, more precise drug discovery and in vivo gene editing. With continued development, including digital twins and large-scale datasets, AI promises a future where personalized therapies for rare and chronic diseases become more accessible and globally equitable.

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