Letting a molecular generator choose its own size
A compact latent model generates molecules of varying size. Its careful separation of reconstruction, refinement and quantum-verified selection shows where the useful candidates come from.
Questions across chemistry.
Explore AI through the reactions, molecules, materials, and methods it touches.
What a reversed sequence can teach a molecular model
An unreviewed study finds that structured practice before chemical training can improve property prediction. Its controls show why the benefit depends on more than extra training.
Generating a molecule is the beginning
SmileyLlama makes molecular generation steerable in words. The next question is how a proposed structure earns experimental trust.
A mass spectrum still leaves a choice
DiffMS generates molecular candidates from a spectrum and a known formula. Its benchmark gains are useful partly because the remaining ambiguity is measurable.
Adding orbitals to the molecular graph
Stereoelectronic features can enrich a molecular representation. A learned surrogate makes the extra information accessible while introducing another prediction boundary.
The test set is part of the question
DataSAIL makes molecular similarity an explicit part of evaluation. Its addendum also shows why no split should be treated as a universal answer.
What a bag of atoms can teach a benchmark
A 2024 study asks how much molecular topology a benchmark really demands, and tests a set-based alternative to familiar graph models.
Zero-shot generation is not data-free
SiMGen generates molecular structures through local similarity. Its use of pretrained components makes the meaning of zero-shot worth spelling out.