rather adverse effects?). There have probably been fewer than 10 million clinical trials in the history of mankind. That’s for all indications, all phases of development, and includes devices.
Secondly, we don’t have a complete understanding of the healthy/normal nervous system, let alone neurological disorders, which usually don’t have clear, definitive causes, biomarkers, or objective diagnostic tests. In other words, if we don’t know what went wrong (and why), how can anyone (or anything) come up with effective, rationally-designed therapies?
Finally, there are about 160 million known chemical compounds. This may sound like a lot, but it’s minuscule when compared with chemical space: every possible drug molecule, both known and unknown. The number of compounds that make up this chemical space is estimated to be 10 to the 60th power. That it is double the number of stars estimated to exist in the entire universe.
Nevertheless, opportunities arise from combining AI with physics-based modeling, as demonstrated by Verseon, a company that uses AI to generate synthetic data and novel drug candidates for drug discovery.
Verseon is leveraging AI and physics-based modeling. The company has created a platform that generates synthetic, drug-like compounds by combining molecular building blocks, which are then tested for their potential to treat diseases. Verseon’s approach allows for the exploration of vast chemical possibilities and the discovery of novel compounds. According to their website, the company currently has 14 drug candidates across various disease categories.
Schrödinger uses physics-based predictions tightly coupled with machine learning methods to accelerate the exploration of chemical space and facilitate the discovery of new compounds. The company has 8 candidates in their pipeline.
Note: I have no relationship with Verseon or Schrödinger (financially or otherwise), I just find their approaches interesting.