smole-bert / README.md
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metadata
license: apache-2.0
datasets:
  - jxie/guacamol
  - AdrianM0/MUV
library_name: transformers

Model Details

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.

Enumeration-aware Molecular Transformers

Introduces contrastive learning alongside multi-task regression, and masked language modelling as pre-training objectives to inject enumeration knowledge into pre-trained language models.

a. Molecular Domain Adaptation (Contrastive Encoder-based)

i. Architecture

smole bert drawio

ii. Contrastive Learning
Screenshot 2023-04-22 at 11 54 23 AM

b. Canonicalization Encoder-decoder (Denoising Encoder-decoder)

Screenshot 2023-04-22 at 11 43 06 AM

Pretraining steps for this model:

  • Pretrain BERT model with Masked language modeling with masked proportion set to 15% on Guacamol datasetFore more details please see our github repository.

  • Virtual Screening Benchmark (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/5/1/26

extended 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/ci400466r

Model List

Our released models are listed as following. You can import these models by using the smiles-featurizers package or using HuggingFace's Transformers.

Model Type AUROC BEDROC
UdS-LSV/smole-bert Bert 0.615 0.225
UdS-LSV/smole-bert-mtr Bert 0.621 0.262
UdS-LSV/smole-bart Bart 0.660 0.263
UdS-LSV/muv2x-simcse-smole-bart Simcse 0.697 0.270
UdS-LSV/siamese-smole-bert-muv-1x SentenceTransformer 0.673 0.274