From the 1 of 5 linked papers with an AI index.
5 papers
SpecCal: Ambiguity-Aware Candidate Calibration for Infrared Spectrum-Based Molecular Structure Reconstruction
Yixuan Chen, Bo Liu, Yusen Tan +3
The paper presents SpecCal, a training‑free, model‑agnostic framework that re‑ranks and augments candidate molecules to improve reconstruction of molecular structures from infrared…
Refold: Refining Protein Inverse Folding with Efficient Structural Matching and Fusion
Yiran Zhu, Changxi Chi, Hongxin Xiang +3
Protein inverse folding aims to design an amino acid sequence that will fold into a given backbone structure, serving as a central task in protein design. Two main paradigms have b…
EDBench: Large-Scale Electron Density Data for Molecular Modeling
Hongxin Xiang, Ke Li, Mingquan Liu +7
Existing molecular machine learning force fields (MLFFs) generally focus on the learning of atoms, molecules, and simple quantum chemical properties (such as energy and force), but…
NovoBench: Benchmarking Deep Learning-based De Novo Peptide Sequencing Methods in Proteomics
Jingbo Zhou, Shaorong Chen, Jun Xia +6
Tandem mass spectrometry has played a pivotal role in advancing proteomics, enabling the high-throughput analysis of protein composition in biological tissues. Many deep learning m…
FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning
Sizhe Liu, Jun Xia, Lecheng Zhang +8
Molecular relational learning (MRL) is crucial for understanding the interaction behaviors between molecular pairs, a critical aspect of drug discovery and development. However, th…