Mehmet Akdel

PLINDER provides a large, annotated PLI dataset with diverse annotations and improved evaluation splits to better assess computational methods for drug design.

PINDER provides a benchmark dataset for evaluating protein-protein interaction docking models, revealing overestimation of performance on leaky test sets and showing improvements with interface-clustered training.

This paper reviews recent advances in deep learning for protein design, presents a practical pipeline for sequence-to-property analysis, and suggests a generated protein sequence for biosynthetic applications.

OptiDiff detects and classifies homozygous and heterozygous structural variations from optical mapping data with better performance than existing methods at low coverage.

This paper introduces two open-source tools for processing and aligning optical mapping data to improve genome assembly and data utilization.

The paper introduces a fast structure aligner using rotation-invariant shape-mers for protein structures, enabling efficient machine learning analysis of functional and structural similarities.

Geometricus embeds protein structures into fixed-length vectors using shape-mers derived from 3D moment invariants, enabling tasks like structure similarity search and clustering.