Attribution

Citations & credits.

Spinhance stands on a lot of prior work. This page tracks the academic references, public data sources, and open-source software the pipeline and this site are built on, so credit is attributed rigorously. Citations are in ACS style.

Data sources

Molecule databases

  1. Kim, S.; Chen, J.; Cheng, T.; Gindulyte, A.; He, J.; He, S.; Li, Q.; Shoemaker, B. A.; Thiessen, P. A.; Yu, B.; Zaslavsky, L.; Zhang, J.; Bolton, E. E. PubChem 2023 update. Nucleic Acids Res. 2023, 51 (D1), D1373–D1380. 8-spin molecule source
  2. Zdrazil, B.; Felix, E.; Hunter, F.; Manners, E. J.; Blackshaw, J.; Corbett, S.; de Veij, M.; Ioannidis, H.; Lopez, D. M.; Mosquera, J. F.; et al. The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods. Nucleic Acids Res. 2024, 52 (D1), D1180–D1192. original screening set
Methods & academic references

Spectral parameters & model

  1. Pretsch, E.; Bühlmann, P.; Badertscher, M. Structure Determination of Organic Compounds: Tables of Spectral Data, 4th rev. and enl. ed.; Springer-Verlag: Berlin, Heidelberg, 2009. shift & coupling constants
  2. Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; Polosukhin, I. Attention Is All You Need. Advances in Neural Information Processing Systems (NeurIPS) 2017, 30, 5998–6008. transformer encoder/decoder
  3. Carion, N.; Massa, F.; Synnaeve, G.; Usunier, N.; Kirillov, A.; Zagoruyko, S. End-to-End Object Detection with Transformers. European Conference on Computer Vision (ECCV) 2020, 213–229. query decoder + set matching
  4. He, K.; Zhang, X.; Ren, S.; Sun, J. Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016, 770–778. 1-D residual encoder
  5. Kuhn, H. W. The Hungarian Method for the Assignment Problem. Naval Research Logistics Quarterly 1955, 2 (1–2), 83–97. bipartite matching loss
Software

Open-source tools

  1. Landrum, G.; et al. RDKit: Open-source Cheminformatics. https://www.rdkit.org. parsing · equivalence · 3D embedding
  2. Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library. Advances in Neural Information Processing Systems (NeurIPS) 2019, 32, 8024–8035. model training
  3. Harris, C. R.; Millman, K. J.; van der Walt, S. J.; Gommers, R.; Virtanen, P.; Cournapeau, D.; et al. Array Programming with NumPy. Nature 2020, 585, 357–362. numerics
  4. Virtanen, P.; Gommers, R.; Oliphant, T. E.; Haberland, M.; Reddy, T.; Cournapeau, D.; et al. SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods 2020, 17, 261–272. assignment · signal
  5. McKinney, W. Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Science Conference 2010, 56–61. pandas
  6. Streamlit Inc. Streamlit: A faster way to build and share data apps. https://streamlit.io. training dashboard
  7. Rego, N.; Koes, D. 3Dmol.js: Molecular Visualization with WebGL. Bioinformatics 2015, 31 (8), 1322–1324. 3D viewer on this site