Skynet with benefits: Can AI and humans become a drug discovery superorganism?

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Will the credit for future mega-blockbuster drugs, in some cases, go to a carefully-programmed AI discovery system connected to a “self-driving lab” that verified its potential?

Certainly, AI is hyped, but so are potential profits of potentially AI-optimized drugs. The exploding volumes of scientific data highlight a shift often overlooked: what does “inventor” even mean when human brilliance relies on AI and vast datasets no single person can comprehend? This future depends in part on connecting the dots between data experts, lab scientists with domain knowledge, and the machine learning systems capable of pattern recognition humans can’t even fathom. But the crux isn’t simply generating more data, and making it a shared, dynamic force fueling breakthrough discoveries — a force deeply integrated with computation and human expertise.

Breaking through the data bottleneck

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PathAI launches AISight digital pathology platform and NSCLC algorithm 

PathAI has launched a new digital pathology platform, AISight. Supporting AI-driven research, the digital pathology platform was trained on a real-world dataset of over 5,000 samples with inputs from more than 350,000 cell and tissue-level annotations from more than 50 pathologists. The company has also released its AIM-PD-L1 NSCLC RUO algorithm, which calculates the percentage of PD-L1 positive tumor and immune cells in NSCLC samples. The AIM-PD-L1 NSCLC RUO algorithm calculates the percentage of PD-L1 positive tumor and immune cells in NSCLC samples over the entire slide image (WSI).

Validating the AIM-PD-L1 NSCLC algorithm

PathAI previously shared validation data for the AIM-PD-L1 NSCLC algorithm at the AACR Annual Meeting in 2022. The AISight Early Access Network laboratories will build on this work by testing the algorithm’s performance on real-world data. Other IHC quantitation algorithms for various types of cancer are also available to these labs. PathAI…

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