Alexander Derry

Alexander Derry

Biomedical Informatics PhD Candidate in Russ Altman's lab at Stanford University, focused on machine learning methods for 3D protein structure analysis.

Publications

You can also find a full list of my publications on my Google Scholar profile.

  1. Derry, A. & Altman, R. B. (2023) “Explainable protein function prediction using local structure embeddings”. BioRxiv. link

  2. Derry, A. & Altman, R. B. (2023) “Unsupervised learning reveals landscape of local structural motifs across protein classes”. BioRxiv. link

  3. Derry, A. & Altman, R. B. (2022). COLLAPSE: A representation learning framework for identification and characterization of protein structural sites. Protein Science, 32(2). link

  4. Derry, A.*, Carpenter, K.*, & Altman, R. B. (2021). “Training data composition affects performance of structure analysis algorithms”. In PACIFIC SYMPOSIUM ON BIOCOMPUTING 2022 (pp. 10-21). link.

  5. Townshend, R. J. L., Vogele, M.*, Suriana, P.*, Derry, A.*, Powers, A., Laloudakis, Y., Balachandar, S., Eismann, S., Altman, R. B., & Dror, R. O. (2021). “ATOM3D: Tasks On Molecules in Three Dimensions”. NeurIPS 2021 Datasets and Benchmarks Track. link

  6. Anand-Achim, N., Eguchi, R. R., Mathews I. I., Perez C. P., Derry, A., Altman, R. B., & Huang, P.-S. (2020). “Protein Sequence Design with a Learned Potential”. bioRxiv. link

  7. Rensi, S., Keys, A., Lo, Y.-C., Derry, A., McInnes, G., Liu, T., & Altman R. B. (2020). “Homology Modeling of TMPRSS2 Yields Candidate Drugs That May Inhibit Entry of SARS-CoV-2 into Human Cells”. ChemRxiv. link

  8. Sosa, D. N.*, Derry, A.*, Guo, M.*, Wei, E., Brinton, C., & Altman, R. B. (2019). “A literature-based knowledge graph embedding method for identifying drug repurposing opportunities in rare diseases”. In PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020 (pp. 463-474). link

  9. Lu, C., Park, S., Richner, T. J., Derry, A., Brown, I., Hou, C., Rao, S., Kang, J., Moritz, C. T., Fink, Y., & Anikeeva, P. (2017). “Flexible and stretchable nanowire-coated fibers for optoelectronic probing of spinal cord circuits”. Science Advances, 3(3). link

In addition to research papers, I have also contributed columns to the Points of Significance series in Nature Methods, which aims to provide accessible explanations of statistical concepts.

  1. Neural Networks Primer: An introduction to the power of artificial neural networks in deriving insights from complex biological data.

  2. Convolutional Neural Networks: By exploiting the inherent spatial structure of data, we can learn robust patterns in a parameter-efficient manner.