Data Scientist, Biological Sciences
Data Scientist, Biological Sciences


Sean Colby is an expert in the application of computational resources to solve multidisciplinary problems, often involving massive datasets (big data). His focus has primarily been in service of “standards free metabolomics,” which involves expanding molecular reference libraries computationally, as opposed to cost- and time- prohibitive experimental acquisition. This research has yielded several open-source software packages, including ISiCLE, the in silico chemical library engine, a quantum-chemistry pipeline for molecular property prediction; DarkChem, a generative deep neural network to predict molecular properties and generate potentially novel metabolites; and DEIMoS, or data extraction for integrated multidimensional spectrometry, to process mass spectrometry data (e.g. LC-MS/MS, LC-IMS-MS/MS) with N-dimensional data as input.

Research Interest

  • Metabolomics
  • Standards- and library- free compound identification
  • Cheminformatics, bioinformatics
  • Quantum chemistry
  • Artificial intelligence, machine learning
  • Computer vision
  • High-performance computing

Disciplines and Skills

  • Cheminformatics
  • Bioinformatics
  • Modeling and simulation
  • Machine learning
  • Deep learning
  • Computer vision


  • BS, Bioengineering, Washington State University
  • MS, Computer Science, Georgia Institute of Technology



  • Schultz, K. J.;  Colby, S. M.;  Yesiltepe, Y.;  Nuñez, J. R.;  McGrady, M. Y.; Renslow, R. S., Application and assessment of deep learning for the generation of potential NMDA receptor antagonists. Physical Chemistry Chemical Physics 2021, 23 (2), 1197-1214.
  • Schultz, K. J.;  Colby, S. M.;  Lin, V. S.;  Wright, A. T.; Renslow, R. S., Ligand-and Structure-Based Analysis of Deep Learning-Generated Potential α2a Adrenoceptor Agonists. Journal of Chemical Information and Modeling 2021, 61 (1), 481-492.
  • Nunez, J.;  Brayfindley, E.;  Colby, S. M.;  McGrady, M.;  Jarman, K. H.;  Renslow, R. S.; Metz, T. O., Collision cross section specificity for small molecule identification workflows. arXiv preprint arXiv:2111.03134 2021.
  • Nielson, F. F.;  Colby, S. M.;  Thomas, D. G.;  Renslow, R. S.; Metz, T. O., Exploring the impacts of conformer selection methods on ion mobility collision cross section predictions. Analytical chemistry 2021, 93 (8), 3830-3838.
  • Nielson, F. F.;  Colby, S. M.;  Renslow, R. S.; Metz, T. O., Similarity Downselection: A Python implementation of a heuristic search algorithm for finding the set of the n most dissimilar items with an application in conformer sampling. arXiv preprint arXiv:2105.02991 2021.
  • Kuprat, A. P.;  Jalali, M.;  Jan, T.;  Corley, R. A.;  Asgharian, B.;  Price, O.;  Singh, R. K.;  Colby, S.; Darquenne, C., Efficient bi-directional coupling of 3D computational fluid-particle dynamics and 1D Multiple Path Particle Dosimetry lung models for multiscale modeling of aerosol dosimetry. Journal of Aerosol Science 2021, 151, 105647.
  • Colby, S. M.;  Chang, C. H.;  Bade, J. L.;  Nunez, J. R.;  Blumer, M. R.;  Orton, D. J.;  Bloodsworth, K. J.;  Nakayasu, E. S.;  Smith, R. D.; Ibrahim, Y. M., DEIMoS: an open-source tool for processing high-dimensional mass spectrometry data. arXiv preprint arXiv:2112.03466 2021.
  • Chang, H.-Y.;  Colby, S. M.;  Du, X.;  Gomez, J. D.;  Helf, M. J.;  Kechris, K.;  Kirkpatrick, C. R.;  Li, S.;  Patti, G. J.; Renslow, R. S., A Practical Guide to Metabolomics Software Development. Analytical chemistry 2021, 93 (4), 1912-1923.
  • Borges, R. M.;  Colby, S. M.;  Das, S.;  Edison, A. S.;  Fiehn, O.;  Kind, T.;  Lee, J.;  Merrill, A. T.;  Merz Jr, K. M.; Metz, T. O., Quantum Chemistry Calculations for Metabolomics: Focus Review. Chemical reviews 2021.
  • Blumer, M. R.;  Chang, C. H.;  Brayfindley, E.;  Nunez, J. R.;  Colby, S. M.;  Renslow, R. S.; Metz, T. O., Mass Spectrometry Adduct Calculator. Journal of Chemical Information and Modeling 2021.
  • Bade, J. L.;  Colby, S. M.;  Renslow, R. S.; Metz, T. O., A Validated Method for Predicting Small Molecule Ionization Sites using Gibb's Free Energies. arXiv preprint arXiv:2111.03141 2021.


  • Rod, K. A.;  Smith, A. P.;  Leng, W.;  Colby, S. M.;  Kukkadapu, R. K.;  Bowden, M. E.;  Qafoku, O.;  Um, W.;  Hochella, M. F.; Bailey, V. L., Water-dispersible nanocolloids and higher temperatures promote the release of carbon from riparian soil. Vadose Zone Journal 2020, 19 (PNNL-SA-156976).
  • Couvillion, S. P.;  Agrawal, N.;  Colby, S. M.;  Brandvold, K. R.; Metz, T. O., Who is metabolizing what? Discovering novel biomolecules in the microbiome and the organisms who make them. Frontiers in Cellular and Infection Microbiology 2020, 10, 388.


  • Colby, S. M.; Nuñez, J. R.; Hodas, N. O.; Corley, C. D.; Renslow, R. S., Deep learning to generate in silico chemical property libraries and candidate molecules for small molecule identification in complex samples. Analytical chemistry 2019.
  • Colby, S. M.; Thomas, D. G.; Nuñez, J. R.; Baxter, D. J.; Glaesemann, K. R.; Brown, J. M.; Pirrung, M. A.; Govind, N.; Teeguarden, J. G.; Metz, T. O., ISiCLE: A quantum chemistry pipeline for establishing in silico collision cross section libraries. Analytical chemistry 2019, 91 (7), 4346-4356.
  • Nunez, J. R.; Colby, S. M.; Thomas, D. G.; Tfaily, M. M.; Toliċ, N.; Ulrich, E. M.; Sobus, J. R.; Metz, T. O.; Teeguarden, J. G.; Renslow, R. S., Evaluation of In Silico Multi‐Feature Libraries for Providing Evidence for the Presence of Small Molecules in Synthetic Blinded Samples. Journal of Chemical Information and Modeling 2019.
  • Rod, K. A.; Um, W.; Colby, S. M.; Rockhold, M. L.; Strickland, C. E.; Han, S.; Kuprat, A. P., Relative permeability for water and gas through fractures in cement. PloS one 2019, 14 (1).


  • Colby, S. M.; McClure, R. S.; Overall, C. C.; Renslow, R. S.; McDermott, J. E., Improving network inference algorithms using resampling methods. BMC bioinformatics 2018, 19 (1), 1-9.
  • Yesiltepe, Y.; Nuñez, J. R.; Colby, S. M.; Thomas, D. G.; Borkum, M. I.; Reardon, P. N.; Washton, N. M.; Metz, T. O.; Teeguarden, J. G.; Govind, N., An automated framework for NMR chemical shift calculations of small organic molecules. Journal of cheminformatics 2018, 10 (1), 1-16.
  • Garcellano, R. C.; Moinuddin, S. G.; Young, R. P.; Zhou, M.; Bowden, M. E.; Renslow, R. S.; Yesiltepe, Y.; Thomas, D. G.; Colby, S. M.; Chouinard, C. D., Isolation of tryptanthrin and reassessment of evidence for its isobaric isostere wrightiadione in plants of the Wrightia Genus. Journal of natural products 2018, 82 (3), 440-448.
  • Nobela, O.; Renslow, R. S.; Thomas, D. G.; Colby, S. M.; Sitha, S.; Njobeh, P. B.; Du Preez, L.; Tugizimana, F.; Madala, N. E., Efficient discrimination of natural stereoisomers of chicoric acid, an HIV-1 integrase inhibitor. Journal of Photochemistry and Photobiology B: Biology 2018, 189, 258-266.


  • White III, R. A.; Rivas-Ubach, A.; Borkum, M. I.; Köberl, M.; Bilbao, A.; Colby, S. M.; Hoyt, D. W.; Bingol, K.; Kim, Y.-M.; Wendler, J. P., The state of rhizospheric science in the era of multi-omics: a practical guide to omics technologies. Rhizosphere 2017, 3, 212-221.
  • White, R. A.; Borkum, M. I.; Rivas-Ubach, A.; Bilbao, A.; Wendler, J. P.; Colby, S. M.; Köberl, M.; Jansson, C., From data to knowledge: the future of multi-omics data analysis for the rhizosphere. Rhizosphere 2017, 3, 222-229.


  • Colby, S. M.; Kabilan, S.; Jacob, R. E.; Kuprat, A. P.; Einstein, D. R.; Corley, R. A., Comparison of realistic and idealized breathing patterns in computational models of airflow and vapor dosimetry in the rodent upper respiratory tract. Inhalation toxicology 2016, 28 (4), 192-202.
  • Kabilan, S.; Suffield, S. R.; Recknagle, K. P.; Jacob, R. E.; Einstein, D. R.; Kuprat, A. P.; Carson, J. P.; Colby, S. M.; Saunders, J. H.; Hines, S., Computational fluid dynamics modeling of Bacillus anthracis spore deposition in rabbit and human respiratory airways. Journal of Aerosol Science 2016, 99, 64-77.


  • Jacob, R. E.; Colby, S. M.; Kabilan, S.; Einstein, D. R.; Carson, J. P., In situ casting and imaging of the rat airway tree for accurate 3D reconstruction. Experimental lung research 2013, 39 (6), 249-257.


  • Carson, J. P.; Kuprat, A. P.; Colby, S. M.; Davis, C. A.; Basciano, C. A.; Greene, K.; Feo, J. T.; Kennedy, A. In Detecting distance between injected microspheres and target tumor via 3D reconstruction of tissue sections, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE: 2012; pp 1149-1152.