Breaking New Ground: ShallowBench Challenges AI in Drug Discovery

JUST IN: ShallowBench is here to shake things up AI-driven drug discovery. While generative AI models have been making waves in designing drugs, they've hit a wall with certain elusive targets. And now, ShallowBench is set to change that.

The Challenge of Low-Pocketability Targets

AI's been scoring big with deep binding pockets. But low-pocketability targets like KRAS and MYC, traditionally dubbed "undruggable", it's a different story. These targets present a unique challenge. They're like trying to fit a square peg in a round hole. The standard AI models just can't cut it.

Meet ShallowBench

Enter ShallowBench, a benchmark that's changing the landscape. With a curation of 5,780 shallow-pocket targets from CrossDocked2020, it's setting a new standard. By comparing Alpha Shape "lid" volumes to protein atom voxel volumes, ShallowBench isolates targets with low concavity but sufficient surface area for binding.

This means generative models have a new playground. But they're not playing well yet. State-of-the-art models show weaker binding affinity predictions on these low-concavity interfaces. It's a wake-up call.

Why This Matters

The labs are scrambling. ShallowBench shows there's a glaring gap in AI drug design. It’s time for new architectures and loss functions. Generative biology models need an upgrade. The leaderboard shifts, and it's clear: innovation is non-negotiable.

But here's the kicker: if AI can't evolve to meet these challenges, are we really pushing the boundaries of what's possible in drug design? It's a question worth pondering.