Accepted Papers

  1. Felix Quintana, Todd Treangen and Lydia Kavraki. Leveraging Large Language Models for Predicting Microbial Virulence from Protein Structure and Sequence.
  2. Maytha Alshammari, Jing He and Willy Wriggers. AlphaFold2 Model Refinement Using Structure Decoys.
  3. Justin Tam, Alexandra Chua, Adyn Gallagher, Denice Omene, Danielle Okun, Dominic DiFranzo and Brian Chen. A Containerization Framework for Bioinformatics Software to Advance Scalability, Portability, and Maintainability.
  4. Dikshant Sagar, Ali Risheh, Nida Sheikh and Negin Forouzesh. Physics-Guided Deep Generative Model For New Ligand Discovery.
  5. Megan Herceg and Amarda Shehu. Structure- and Energy-based Analysis of Small Molecule Ligand Binding to Nuclear Steroid Hormone Receptors.
  6. Changrui Li and Filip Jagodzinski. Identifying Impactful Pairs of Insertion Mutations in Proteins.
  7. Nguyen Quoc Khanh Le and Quang Hien Kha. A Sequence-Based Prediction Model of Vesicular Transport Proteins Using Ensemble Deep Learning.
  8. Sarah Coffland, Katie Christensen, Filip Jagodzinski and Brian Hutchinson. RoseNet: Predicting Energy Metrics of Double InDel Mutants Using Deep Learning.
  9. Cory Kromer-Edwards. Optimizing K-Mer Fingerprint Generation for Machine Learning.
  10. Anowarul Kabir, Asher Moldwin and Amarda Shehu. A Comparative Analysis of Transformer-based Protein Language Models for Remote Homology Prediction.
  11. Thu Nguyen, Yongcheng Mu, Jiangwen Sun and Jing He. An Approach to Developing Benchmark Datasets for Protein Secondary Structure Segmentation from Cryo-EM Density Maps.
  12. Usman Abbas, Jin Chen and Qing Shao. Assessing Fairness of AlphaFold2 Prediction of Protein 3D Structures