We introduce a suite of neural language model tools for pre-training, fine-tuning SMILES-based molecular language models. Furthermore, we also provide recipes for semi-supervised recipes for fine-tuning these languages in low-data settings using Semi-supervised learning.
Introduces contrastive learning alongside multi-task regression, and masked language modelling as pre-training objectives to inject enumeration knowledge into pre-trained language models.
Fore more details please see our
github repository.
original version presented in
S. Riniker, G. Landrum, J. Cheminf., 5, 26 (2013),
DOI: 10.1186/1758-2946-5-26,
URL:
http://www.jcheminf.com/content/5/1/26extended version presented in
S. Riniker, N. Fechner, G. Landrum, J. Chem. Inf. Model., 53, 2829, (2013),
DOI: 10.1021/ci400466r,
URL:
http://pubs.acs.org/doi/abs/10.1021/ci400466rOur released models are listed as following. You can import these models by using the
smiles-featurizers package or using
HuggingFace's Transformers.
