Insilico Medicine
New York
United States
Overview
Some companies in the AI space innovate by creating amazing technological infrastructure, such as next-generation algorithms. Others focus less on the research side of things, but instead on applying existing technology to critical real-world problems. Insilico Medicine does both. This Hong Kong-based biotech company has been working toward its mission of using AI for drug discovery since 2014.
Given how time-consuming and expensive classical drug discovery is, the idea that artificial intelligence could be used as part of the development process is one that researchers, clinicians and, yes, investors have been excited about for many years. Among other possible advances, AI can help analyze large volumes of data to identify possible drug candidates with higher levels of accuracy and speed.
The challenge is that, even if AI can help develop futuristic drugs, the high tech mantra of “move fast and break things” doesn’t fit wholly comfortably in the world of medicine, with its stringent safety and efficacy requirements. As a result, especially in a depressed economy, many startups in this sphere face a kind of “biotechnology winter” in which they risk running out of funding long before they can reach the point of creating anything.
Early mover Insilico Medicine appears not to have this problem. Having raised upward of $400 million, it’s seemingly cash-rich, and bringing in revenue through partnerships with various pharmaceutical companies, including China-based Fosun Pharma and French multinational pharmaceutical company Sanofi, for the use of its AI platforms. It’s employing a range of generative AI technologies to create novel molecular structures with desired properties, and is tackling a wide range of medical problems including cancer, fibrosis, autoimmune diseases, and more. Since 2021, Insilico has announced at least 12 preclinical drug candidates, meaning drugs with enough supporting evidence to be considered for human testing. Of these, three have so far advanced to human clinical trials and, as revealed in June, one such drug—billed as being the world’s first anti-fibrotic small molecule inhibitor designed using generative AI—has now graduated to Phase II clinical trials.
FIND OUT MORE
Given how time-consuming and expensive classical drug discovery is, the idea that artificial intelligence could be used as part of the development process is one that researchers, clinicians and, yes, investors have been excited about for many years. Among other possible advances, AI can help analyze large volumes of data to identify possible drug candidates with higher levels of accuracy and speed.
The challenge is that, even if AI can help develop futuristic drugs, the high tech mantra of “move fast and break things” doesn’t fit wholly comfortably in the world of medicine, with its stringent safety and efficacy requirements. As a result, especially in a depressed economy, many startups in this sphere face a kind of “biotechnology winter” in which they risk running out of funding long before they can reach the point of creating anything.
Early mover Insilico Medicine appears not to have this problem. Having raised upward of $400 million, it’s seemingly cash-rich, and bringing in revenue through partnerships with various pharmaceutical companies, including China-based Fosun Pharma and French multinational pharmaceutical company Sanofi, for the use of its AI platforms. It’s employing a range of generative AI technologies to create novel molecular structures with desired properties, and is tackling a wide range of medical problems including cancer, fibrosis, autoimmune diseases, and more. Since 2021, Insilico has announced at least 12 preclinical drug candidates, meaning drugs with enough supporting evidence to be considered for human testing. Of these, three have so far advanced to human clinical trials and, as revealed in June, one such drug—billed as being the world’s first anti-fibrotic small molecule inhibitor designed using generative AI—has now graduated to Phase II clinical trials.
