Brandon Anderson

Ian Quigley × Brandon Anderson × RJ Honicky

Leash’s reported edge is low-cost, carefully characterized data: its ten-person operation developed about 2,000 protein constructs and ran roughly 5,000 screens.On the hardest Kaggle split, none of roughly 2,000 teams beat chance, exposing how poorly models transfer to distant chemistry.Language agents now test hypotheses instantly against measured compounds, but coverage remains limited and potency calibration unfinished.

Pushmeet Kohli × Sal Candido × Brandon Anderson

AlphaFold 2 reproduced PDB-deposited structures, but ground state, dynamics, function, and design remain open, shifting the frontier toward richer biological context and raw cryo-EM data.Metagenomic sequences show useful scaling depends on statistical fit rather than volume, while calibrated pLDDT and clearer bottlenecks—not a 10x or 100x timetable—determine whether AI can safely accelerate drug development.

Brandon Anderson × Evan Feinberg × Sergey Edunov

Genesis Molecular AI is applying diffusion and inference-time scaling to 3D molecular design, targeting roughly 1 Å protein-ligand accuracy because 2 Å can hide chemically fatal errors.Its prospective edge comes from Incyte and Insitro programs that connect models to synthesis and ADMET feedback, while OpenBind showed stronger unseen-target generalization and GPUs remain the scaling bottleneck.

Brandon Anderson × RJ Honicky × Heather Kulik

Materials AI lacks an AlphaFold-like shortcut: variable bonding and sparse experimental ground truth make validation a central bottleneck.AI found a polymer-network design that made the material about four times tougher through electron rearrangement during molecular breakage.Active learning offers at least a hundred- to thousandfold speedup across seven direct-air-capture objectives, but reliable DFT replacement at two orders of magnitude greater speed and device-scale processing remain unresolved.

RJ Honicky × Brandon Anderson × Andrew White

FutureHouse’s Cosmos turns scientific discovery into a closed loop linking literature, data analysis, experiments and an evolving world model, shifting the bottleneck toward laboratory state, reagent logistics and experiment turnaround.Robin’s dry-AMD work showed verification can beat expert enthusiasm, while Ether0 exposed adversarial verifier failures; scaling discovery will depend on provenance, cheap filtering and robust tests before wet-lab spending.