Efficient Adaptation of Chemical Language Models for Molecular Property Prediction

Chemical language models (CLMs) like ChemBERTa and Molformer enable compound property prediction, but their computational demands limit adoption in resource-constrained settings. We integrate Low-Rank Adapters (LoRA) with CLMs to significantly reduce trainable parameters required for finetuning. Results: We observe 3–5% area under the receiver operating curve (AUC) improvement across classification tasks for molecule toxicity, blood-brain barrier permeability, and flavor prediction over Molformer-XL. Our approach achieves Matthews correlation coefficient (MCC) scores of 0.80–0.90 across three tasks while reducing model parameters by 75–95%. By comparing embeddings from zero-shot and finetuned CLMs combined with molecular physicochemical properties, we attain optimal performance across four datasets. This lightweight adaptation retains performance efficiency while reducing over-parameterization.