Author

Karim Abbasi

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Open access Sep 2026

RA-PLA: Retrieval-augmented graph convolutional networks for protein-ligand binding affinity prediction

A novel end-to-end framework that retrieves hard protein-ligand pairs—samples whose nearest neighbors have conflicting labels under manifold smoothness constraints—and integrates them via a semi-supervised graph convolutional network (GCN) that consistently outperforms state-of-the-art approaches.

Karim Abbasi, Hossein Banadkuki, R. Sepahvand et al. · PLoS ONE · 0 citations

Open access Aug 2026

Shedding light on neural learning to rank models for anticancer drug prioritization

This study systematically benchmark six ranking loss functions, including state-of-the-art listwise methods, and five types of molecular representations across two large-scale drug screening datasets, CTRP and PRISM, to demonstrate that listwise loss functions such as LambdaLoss and LambdaRank consistently excel in bot...

Faraz Sarmeili, Benyamin Ghahremani-Nezhad, Mohammad Khalilpour et al. · PLoS ONE · 0 citations