Chemlactica-125M is a 125M-parameter causal language model for molecular generation, property conditioning, property prediction, and optimization. It continues pretraining from Galactica-125M on PubChemForLM: 40 billion tokens describing more than 110 million PubChem molecules, computed properties, and molecular similarities.
The similarity field is reference SMILES, a space, then the target ECFP Tanimoto similarity. To predict a property, provide the molecule and open the desired property tag:
Common trained tags include [SAS], [QED], [WEIGHT], [TPSA], [CLOGP], [FORMULA], [RINGCOUNT], and [SIMILAR]. Use only tags present in the training format; arbitrary natural-language instructions or oracle names are unsupported.
For optimization, use the Mol-E implementation and canonical PMO prompt registry. In the five-seed PMO-10K reproduction, this checkpoint obtains summed Top-10 AUC scores of 16.944598 (task-agnostic) and 20.429421 (task-informed) across 23 tasks.
Intended use and limitations
This checkpoint is intended for research on small-molecule language modeling and optimization. Generated strings may be invalid, duplicated, chemically implausible, difficult to synthesize, unsafe, or outside the requested property range. Reported properties are predictions or optimization-oracle outputs, not experimental measurements. PubChem-derived data may contain errors or biases, and the model has no knowledge of changes after the cutoff date. Do not use it for clinical decisions or as a substitute for experimental and expert review.
