Benchmarking Large Language Models for Predicting Therapeutic Antisense Oligonucleotide Efficacy

Abstract

Antisense oligonucleotides (ASOs) are a promising class of therapeutic drugs that can target and modulate genes associated with various diseases. This study benchmarks Large Language Models (LLMs) for predicting ASO therapeutic efficacy through a two-stage approach: (1) molecular embedding-based fine-tuning using SMILES representations, and (2) prompt engineering with zero-shot and few-shot learning using DNA sequences with target gene information. We evaluated general-purpose models (GPT-3.5-Turbo, LLaMA2-7B, Galactica-6.7B) and chemistry-specific models (ChemBERTa, Molformer, BERT) across three datasets: PFRED (522 sequences), openASO (1708 sequences), and ASOptimizer (1267 sequences). DNA sequence inputs with target gene information outperformed SMILES representations. GPT-3.5-Turbo achieved R2 values of 0.6381 (PFRED) and 0.6340 (ASOptimizer) for few-shot prompting with k=3 examples. Code and datasets available at: https://github.com/asundar0128/IndependentStudy