ChemBERTa-2: Fine-Tuning for Molecule’s HIV Replication Inhibition Prediction

15 September 2023, Version 1
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

Two versions of Large Language ChemBERTa-2 models, pre-trained with two different methods, were fine-tuned in this work for HIV replication inhibition prediction. The best model achieved AUROC of 0.793. The changes in distributions of molecular embeddings prior to and following fine-tuning reveal models’ enhanced ability to differentiate between active and inactive HIV molecules.

Keywords

HIV inhibition
Large Language Models
ChemBERTA

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