MoLFormer-XL-both-10pct EU AI Act Compliance Profile

MoLFormer-XL-both-10%

MoLFormer is a class of models pretrained on SMILES string representations of up to 1.1B molecules from ZINC and PubChem. This repository is for the model pretrained on 10% of both datasets.

It was introduced in the paper Large-Scale Chemical Language Representations Capture Molecular Structure and Properties by Ross et al. and first released in this repository.

Model Details

Model Description

MoLFormer is a large-scale chemical language model designed with the intention of learning a model trained on small molecules which are represented as SMILES strings. MoLFormer leverges masked language modeling and employs a linear attention Transformer combined with rotary embeddings.

An overview of the MoLFormer pipeline is seen in the image above. One can see that the transformer-based neural network model is trained on a large collection of chemical molecules represented by SMILES sequences from two public chemical datasets PubChem and ZINC in a self-supervised fashion. The MoLFormer architecture was designed with an efficient linear attention mechanism and relative positional embeddings with the goal of learning a meaningful and compressed representation of chemical molecules. After training the MoLFormer foundation model was then adopted to different downstream molecular property prediction tasks via fine-tuning on task-specific data. To further test the representative power of MoLFormer, the MoLFormer encodings were used to recover molecular similarity, and analysis on the correspondence between the interatomic spatial distance and attention value for a given molecule was performed.

Intended use and limitations

You can use the model for masked language modeling, but it is mainly intended to be used as a feature extractor or to be fine-tuned for a prediction task. The "frozen" model embeddings may be used for similarity m

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