cambridgeltl/SapBERT-from-PubMedBERT-fulltext

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.

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…

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.

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.

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

Neeto-1.0-8b is an openly released biomedical large language model (LLM) created by BYOL Academy to assist learners and practitioners with medical exam study, literature understanding, and structured clinical reasoning.