Open Discovery Challenge Tests AI-Designed Malaria Drug Candidates With 6-Axis Scoring

VIDRAFT and FINAL-Bench have launched the Open Discovery Challenge, a public leaderboard evaluating AI-generated drug candidates targeting PfDHODH, a key enzyme in the malaria parasite. The initiative addresses a growing gap in AI drug discovery: while generative models can propose thousands of molecules daily, reliably verifying their potency, selectivity, safety, and synthesizability remains unsolved. Submissions are scored across six axes — whole-cell activity, target binding, selectivity, ADMET profile, novelty, and synthesis feasibility — with detailed methodology published on Hugging Face. During scorer validation, the team identified 14 defects, including toxicity thresholds that incorrectly rejected all three approved antimalarials and a binding-efficiency metric that over-rewarded small, weak molecules like caffeine. The challenge highlights that building a fair, scientifically rigorous automated judge for AI-designed molecules is as hard as the molecule generation itself.