gbyuvd/chemselfies-base-bertmlm

https://huggingface.co/gbyuvd/chemselfies-base-bertmlm
Idleby gbyuvd581updated 1 year ago
Python

This model is a lightweight model pre-trained on SELFIES (Self-Referencing Embedded Strings) representations of molecules. It is trained on 2.7M unique and valid molecules taken from COCONUTDB and ChemBL34, with 7.3M total generated masked examples.

Sourced from

  • HuggingFace — gbyuvd/chemselfies-base-bertmlm

Related resources

This model is a BERT-like sequence classifier for 221 human protein drug targets, fine-tuned from gbyuvd/chemselfies-base-bertmlm on a dataset derived ChemBL34 (Zdrazil et al. 2023). It predicts potential drug targets using chemical structures represented as SELFIES (Self-Referencing Embedded…

Stale392 years ago
Python

In recent years, pre-trained language models (PLMs) achieve the best performance on a wide range of natural language processing (NLP) tasks. While the first models were trained on general domain data, specialized ones have emerged to more effectively treat specific domains.

Stale883 years ago
Python

Target-Conditioned Molecular Ideation Model for Drug Discovery Research

Active04 months ago
Python

ChemFIE-SA is a BERT-like sequence classifier for predicting synthesis accessibility given a SELFIES string of a compound, fine-tuned from gbyuvd/chemselfies-base-bertmlm on DeepSA's expanded dataset from Wang et al. 2023.

Stale82 years ago
Python

SmileBERTa is a RoBERTa-based language model for chemistry, built on the ChemBERTa architecture and fine-tuned to predict small-molecule fragment SMILES from full small-molecule drug SMILES.

Active601 month ago
Python

This is a Japanese RoBERTa base model pre-trained on academic articles in medical sciences collected by Japan Science and Technology Agency (JST).

Stale1463 years ago
Python