ArticleJournal of chemical information and modeling2025
SELFprot: Effective and Efficient Multitask Finetuning Methods for Protein Parameter Prediction.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Synthetic and Engineering Biology in Australia: Advances in Infrastructure, Coordination and Impact.Engineering biologyReview
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3 authors.
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Abstract
Accurately predicting protein-ligand interactions and enzymatic kinetics remains a challenge for computational biology. Here, we present SELFprot, a suite of modular transformer-based machine learning architectures that leverage the ESM2-35M model architecture for protein sequence and small molecule embeddings to improve predictions of complex biochemical interactions. SELFprot employs multitask learning and parameter-efficient finetuning through low-rank adaptation, allowing for adaptive, data-driven model refinement. Furthermore, ensemble learning techniques are used to enhance the robustness and reduce the prediction variance. Evaluated on the BindingDB and CatPred-DB data sets, SELFprot achieves competitive performance with notable improvements in parameter-efficient prediction of
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