Most AI in drug discovery still runs into the same problem: the chemistry gets hard, the simulations get slow, and researchers end up waiting days or weeks for answers they’re not fully confident in. Qubit Pharmaceuticals just published a new paper in Communication Chemistry, part of the Nature Portfolio, tackling exactly that problem. The team developed a method called Dual-LAO that dramatically speeds up one of the most important parts of computational drug discovery: predicting how strongly potential drug molecules bind to a target protein. What does that mean in plain English? it helps researchers test more molecular ideas, more quickly, while keeping the accuracy high enough to matter in real-world drug discovery. What makes this interesting isn’t just the speedup (15–30x faster than many current approaches). It’s that the method also handles some of the hardest problems in molecular simulation, like major structural changes, buried water rearrangements or charge changes. These are the kinds of edge cases where traditional approaches often struggle or break down entirely. The bigger picture is that better simulation changes the economics of drug discovery because if researchers can rule out weak candidates earlier, explore more chemical space, and make decisions faster, you accelerate the path toward viable therapeutics. Learn more in the paper (link in the comments) and congrats to Narjes A., Félix Aviat, Jérôme Hénin, Jean-Philip Piquemal and Louis Lagardère on the publication of their research. #QuantumAI #Quantum #AI
Jérôme Hénin
3mo
It is a pleasure to work with the talented people at Qubit!
Bravo!
Impressive advancement in computational drug discovery. Faster and more accurate molecular simulation can greatly accelerate healthcare innovation. At THANASI Infotech Private Limited we see strong potential in scalable deep-tech platforms supporting such breakthroughs. Congrats to the Qubit Pharmaceuticals team.