Data-driven molecular design

Artificial intelligence and machine learning approaches present enormous potential for data-driven understanding and design of functional molecular materials. We have developed deep generative models for data-driven protein design, interpretable nearest-neighbor models for surface hydrophobicity, active learning platforms for immunomodulator discovery, high-throughput virutal screening platforms for peptide chassis material engineering, Markov state models in slow modes for sequence-engineering of DNA oligomers, and long-time kinetic models for stabilization of desired optical matter patterns.

We are pursuing the following projects in this theme:

  • Data-driven understanding and design of NF-KB and IRF immunomodulators
  • Molecular governance of T-cell fate
  • Engineering of ultra-stable peptide chassis materials for synthetic cells
  • Control of vacancy and defect dynamics in crystals
  • Control of MOF topology by modulation of cationic counterions
  • Machine learning-guided discovery of selective and specific probes for PFAS contaminants and pharmaceutical analytes

Representative Publications

106.  W. Alvarado, V. Agrawal, W.S. Li, V.P. Dravid, V. Backman, J.J. de Pablo, and A.L. Ferguson* “Denoising autoencoder trained on simulation-derived structures for noise reduction in chromatin scanning transmission electron microscopy” ACS Cent. Sci. (accepted, 2023) [ https://doi.org/10.1021/acscentsci.3c00178 ]

99.     Y. Ma, R. Kapoor, B. Sharma, A.P. Liu, and A.L. Ferguson* “Computational design of self-assembling peptide chassis materials for synthetic cells” Mol. Syst. Des. Eng. 8 39-52 (2023) [ https://dx.doi.org/10.1039/D2ME00169A ]

96.     N.B. Rego, A.L. Ferguson*, and A.J. Patel “Learning the relationship between nanoscale chemical patterning and hydrophobicity” Proc. Natl. Acad. Sci. USA 119 48 e2200018119 (2022) [ https://doi.org/10.1073/pnas.2200018119 ]

95.     S. Chen, J.A. Parker, C.W. Peterson, S.A. Rice, N.F. Scherer, and A.L. Ferguson* “Understanding and design of non-conservative optical matter systems using Markov state models” Mol. Sys. Des. Eng. 7 1228-1238 (2022) [ http://dx.doi.org/10.1039/D2ME00087C ]

94.     K. Shmilovich, S.S. Panda, A. Stouffer, J.D. Tovar, and A.L. Ferguson* “Hybrid computational-experimental data-driven design of self-assembling π-conjugated peptides” Digital Discovery 1 448-462 (2022) [ https://dx.doi.org/10.1039/d1dd00047k ]

92.     K. Shmilovich, Y. Yao, J.D. Tovar, H.E. Katz, A. Schleife, and A.L. Ferguson* “Computational discovery of high charge mobility self-assembling π-conjugated peptides” Mol. Syst. Des. Eng. 7 447-459 (2022) [ http://dx.doi.org/10.1039/D2ME00017B ]

→ Selected by editors as MSDE HOT article

91.     B. Mohr, K. Shmilovich, I.S. Kleinwächter, D. Schneider, A.L. Ferguson*, and T. Bereau “Data-driven discovery of cardiolipin-selective small molecules by computational active learning” Chem. Sci. 13 4498-4511 (2022) [ http://dx.doi.org/10.1039/D2SC00116K ]

→ Selected for 2022 ChemSci “Pick of the Week” collection
→ Featured in commentary M. Aldeghi and C.W. Coley “A focus on simulation and machine learning as complementary tools for chemical space navigation” Chem. Sci. (2022) [ https://doi.org/10.1039/d2sc90130g ]

90.    S. Dasetty, I. Coropceanu, J. Porter, J. Li, J.J. de Pablo, D. Talapin, and A.L. Ferguson* “Active learning of polarizable nanoparticle phase diagrams for the guided design of triggerable self-assembling superlattices” Mol. Syst. Des. Eng. 7 350 – 363 (2022) [ http://dx.doi.org/10.1039/D1ME00187F ]

→ Selected by editors as MSDE HOT article

87.     M.S. Jones, B. Ashwood, A. Tokmakoff, and A.L. Ferguson* “Determining sequence-dependent DNA oligonucleotide hybridization and dehybridization mechanisms using coarse-grained molecular simulation, Markov state models, and infrared spectroscopy” J. Am. Chem. Soc. 143 17395-17411 (2021) [ https://doi.org/10.1021/jacs.1c05219 ]

68.     K. Shmilovich, R.A. Mansbach, H. Sidky, O.E. Dunne, S.S. Panda, J.D. Tovar, and A.L. Ferguson* “Discovery of self-assembling π-conjugated peptides by active learning-directed coarse-grained molecular simulation” J. Phys. Chem. B 124 3873-3891 (2020) [ https://doi.org/10.1021/acs.jpcb.0c00708 ]

→ Invited submission to the “Machine Learning in Physical Chemistry” special issue
→ Selected as ACS Editors’ Choice article (March 30, 2020)
→ Selected for front cover art of JPCB vol. 124, issue 19 (May 14, 2020)

39.     A.W. Long and A.L. Ferguson* “Rational design of patchy colloids via landscape engineering” Mol. Syst. Des. Eng. 3 1 49-65 (2018) [ http://dx.doi.org/10.1039/C7ME00077D ]

→ Invited submission to the 2018 Emerging Investigators issue
→ Selected for inside front cover image
→ Selected by journal as winner of RSC MSDE Emerging Investigator Award
→ Awarded the Institution of Chemical Engineers 2018/19 Junior Moulton Medal

33.     W.F. Reinhart, A.W. Long, M.P. Howard, A.L. Ferguson, and A.Z. Panagiotopoulos “Machine learning for autonomous crystal structure identification” Soft Matter 13 4733-4745 (2017) [ http://dx.doi.org/10.1039/c7sm00957g ]

28.     E.Y. Lee, B.M. Fulan, G.C.L. Wong, and A.L. Ferguson* “Mapping membrane activity in undiscovered peptide sequence space using machine learning” Proc. Natl. Acad. Sci. USA 113 48 13588-13593 (2016) [ http://dx.doi.org/10.1073/pnas.1609893113 ]

11.      A.W. Long and A.L. Ferguson* “Nonlinear machine learning of patchy colloid self-assembly mechanisms and pathways” J. Phys. Chem. B 118 15 4228-4244 (2014) [ http://dx.doi.org/10.1021/jp500350b ]