Do Large Scale Molecular Language Representations Capture Important
Structural Information?
- URL: http://arxiv.org/abs/2106.09553v1
- Date: Thu, 17 Jun 2021 14:33:55 GMT
- Title: Do Large Scale Molecular Language Representations Capture Important
Structural Information?
- Authors: Jerret Ross, Brian Belgodere, Vijil Chenthamarakshan, Inkit Padhi,
Youssef Mroueh, Payel Das
- Abstract summary: We present molecular embeddings obtained by training an efficient transformer encoder model, referred to as MoLFormer.
Experiments show that the learned molecular representation performs competitively, when compared to graph-based and fingerprint-based supervised learning baselines.
- Score: 31.76876206167457
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Predicting chemical properties from the structure of a molecule is of great
importance in many applications including drug discovery and material design.
Machine learning based molecular property prediction holds the promise of
enabling accurate predictions at much less complexity, when compared to, for
example Density Functional Theory (DFT) calculations. Features extracted from
molecular graphs, using graph neural nets in a supervised manner, have emerged
as strong baselines for such tasks. However, the vast chemical space together
with the limited availability of labels makes supervised learning challenging,
calling for learning a general-purpose molecular representation. Recently,
pre-trained transformer-based language models (PTLMs) on large unlabeled corpus
have produced state-of-the-art results in many downstream natural language
processing tasks. Inspired by this development, here we present molecular
embeddings obtained by training an efficient transformer encoder model,
referred to as MoLFormer. This model was employed with a linear attention
mechanism and highly paralleized training on 1D SMILES sequences of 1.1 billion
unlabeled molecules from the PubChem and ZINC datasets. Experiments show that
the learned molecular representation performs competitively, when compared to
existing graph-based and fingerprint-based supervised learning baselines, on
the challenging tasks of predicting properties of QM8 and QM9 molecules.
Further task-specific fine-tuning of the MoLFormerr representation improves
performance on several of those property prediction benchmarks. These results
provide encouraging evidence that large-scale molecular language models can
capture sufficient structural information to be able to accurately predict
quantum chemical properties and beyond.
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