GP MoLFormer Uniq GP MoLFormer is a class of models pretrained on SMILES string representations of 0.65 1.1B molecules from ZINC and PubChem. This repository is for the model pretrained on all the unique molecules from both datasets. It was introduced in the paper GP MoLFormer: A Foundation Model For Molecular Generation by Ross et al. and released in this repository. Model Details Model Description GP MoLFormer is a large scale autoregressive chemical language model intended for molecule generation tasks. GP MoLFormer employs the same architecture as MoLFormer XL, including linear attention and rotary position embeddings, but uses decoder only Transformer blocks trained with a causal language modeling objective. It is trained on up to 1.1B molecules in SMILES representation. GP MoLFormer was evaluated on de novo generation ( at scale ), scaffold constrained decoration, and molecular property optimization tasks. Intended use and limitations The pretrained model may be used out of the box for unconditional, de novo molecule generation. It can also be prompted with a partial SMILES string to do scaffold completion/decoration. We also demonstrate it can be fine tuned on a particular d…
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