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AmesNet’s Task-Conditioned Learning architecture outperforms FDA, MIT, Tencent, and the University of Sydney models in both sensitivity and balanced accuracy on out-of-domain chemical data on a public dataset; AmesNet is built on top of ChemPrint, the core and proprietary deep-learning engine of the GALILEO platform that powers the company’s drug pipeline.

● CANDID-CNS™ is the new state-of-the-art AI model for BBB penetration: it achieves 87% AUPRC on bRo5 small molecules compared to 56% by Pfizer’s CNS MPO. ● CANDID-CNS™ distinguishes CNS penetrant stereoisomers at 68% compared to 50% by Pfizer’s CNS MPO. ● Published in the Journal of Chemical Information and Modeling, CANDID-CNS™ presents a breakthrough in AI Drug Discovery by unlocking the potential of bRo5 and stereochemistry to significantly expand the chemical space of small molecules that can be developed into CNS therapeutics.

In AstraZeneca’s third trip to Asia this year, the pharma secured ex-China rights to a dual inhibitor of PDE3 and PDE4, which in a Phase 2b study significantly improved lung function and lowered symptom burden in patients with chronic obstructive pulmonary disorder.