CMU researchers released SMDD-Bench, a benchmark of 502 small-molecule drug design tasks that use RDKit, ADMET-AI, and Boltz-2 as feedback loops. The authors argue that long-horizon planning, exploration, and learning from imperfect feedback remain open problems beyond math and coding, and the benchmark is available in Prime Intellect's Environments Hub for training with prime-rl.
RL needs more long-horizon tasks beyond math and coding. @niloofar_mire's team at CMU built an environment for drug design.
SMDD-Bench comprises 502 small-molecule design tasks with RDKit, ADMET-AI and Boltz-2 in the loop. The challenges go beyond chemistry: long-horizon planning, exploration, and learning from imperfect feedback are also open problems for RL/ML!
SMDD-Bench is available in our Environments Hub, ready to train with prime-rl. Thank you for sharing with the community!
One of the pivots I did over a year ago when I started as Faculty @LTIatCMU and @CMUEngineering was towards AI for Molecule Design and Drug Discovery, and today we are releasing a blog post + our @PrimeIntellect and @harborframework environments for our benchmark SMDD! We find that: 1) Harness optimization can go a super long way in scientific tasks, and we are still heavily bottlenecked by the model capabilities rather than the knowledge, 2) Automating the harness optimization can work on some cases and not others! Amazing work led by @SureshRaghu07, w/ @KevinH1119568 and @aviral_kumar2
Source: Prime Intellect · x.comPublished · added here
