Review articles
98. A.L. Ferguson* and J.D. Tovar “Evolution of pi-peptide assembly: from understanding to prediction and control” Langmuir 38 50 15463-15475 (2022) [ https://doi.org/10.1021/acs.langmuir.2c02399 ]
97. L. Shao, J. Ma, J. Prelesnik, Y. Zhou, M. Nguyen, M. Zhao, S. Jenekhe, S. Kalinin, A.L. Ferguson, J. Pfaendtner, C. Mundy, J. De Yoreo, F. Baneyx, and C.-L. Chen “Hierarchical materials from high information content macromolecular building blocks: construction, dynamic interventions, and prediction” Chemical Reviews 122 24 17397-17478 (2022) [ https://doi.org/10.1021/acs.chemrev.2c00220 ]
93. A.L. Ferguson* and K.A. Brown “Data-driven design and autonomous experimentation in soft and biological materials engineering” Annu. Rev. Chem. Biomol. Eng. 13 25-44 (2022) [ https://doi.org/10.1146/annurev-chembioeng-092120-020803 ]
83. A.L. Ferguson* and R. Ranganathan “100th Anniversary of Macromolecular Science Viewpoint: Data-driven protein design” ACS Macro. Lett. 10 327-340 (2021) [ https://dx.doi.org/10.1021/acsmacrolett.0c00885 ]
→ Invited Viewpoint article for 2020 special collection 100th Anniversary of Macromolecular Science
→ Selected for front cover art of ACS Macro. Lett. vol. 10, issue 4 (April 20, 2021)
→ Featured in editorial review M. Müller “Selection of advances in theory and simulation during the first decade of ACS Macro Letters” ACS Macro Lett. 10 1629-1635 (2021) [ https://doi.org/10.1021/acsmacrolett.1c00750 ]
76. B. Sharma, Y. Ma, A.L. Ferguson*, and A.P. Liu “In search of a novel chassis material for synthetic cells: Emergence of synthetic peptide compartment” Soft Matter 16 10769 (2020) [ https://dx.doi.org/10.1039/D0SM01644F ]
72. P. Gkeka, G. Stoltz, A. Barati Farimani, Z. Belkacemi, M. Ceriotti, J. Chodera, A. Dinner, A.L. Ferguson, J.-B. Maillet, H. Minoux, C. Peter, F. Pietrucci, A. Silveira, A. Tkatchenko, Z. Trstanova, R. Wiewiora, and T. Lelievre “Machine learning force fields and coarse-grained variables in molecular dynamics: Application to materials and biological systems” J. Chem. Theory Comput. 16 8 4757–4775(2020) [ https://doi.org/10.1021/acs.jctc.0c00355 ]
66. H. Sidky, W. Chen, and A.L. Ferguson* “Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation” Molecular Physics 118 5 e1737742 (2020) [ https://doi.org/10.1080/00268976.2020.1737742 ]
→ Invited New Views article in Molecular Physics
65. M. Magana, M. Pushpanathan, A. Santos, L. Lense, M. Fernandez, A. Ioannidis, M.A. Giulianotti, Y. Apidianakis, S. Bradfute, A.L. Ferguson, A. Cherkasov, M.N. Seleem, C. Pinilla, C. de la Fuente-Nunez, T. Lazaridis, T. Dai, R.A. Houghten, R.E.W. Hancock, and G.P. Tegos “The value of antimicrobial peptides in the age of resistance” The Lancet Infectious Diseases (2020) [ https://doi.org/10.1016/S1473-3099(20)30327-3 ]
62. The PLUMED Consortium (M. Bonomi, G. Bussi, C. Camilloni, G.A. Tribello, P. Banáš, A. Barducci, M. Bernetti, P.G. Bolhuis, S. Bottaro, D. Branduardi, R. Capelli, P. Carloni, M. Ceriotti, A. Cesari, H. Chen, W. Chen, F. Colizzi, S. De, M. De La Pierre, D. Donadio, V. Drobot, B. Ensing, A.L. Ferguson, M. Filizola, J.S. Fraser, H. Fu, P. Gasparotto, F. Luigi Gervasio, F. Giberti, A. Gil-Ley, T. Giorgino, G.T. Heller, G.M. Hocky, M. Iannuzzi, M. Invernizzi, K.E. Jelfs, A. Jussupow, E. Kirilin, A. Laio, V. Limongelli, K. Lindorff-Larsen, T. Löhr, F. Marinelli, L. Martin-Samos, M. Masetti, R. Meyer, A. Michaelides, C. Molteni, T. Morishita, M. Nava, C. Paissoni, E. Papaleo, M. Parrinello, J. Pfaendtner, P. Piaggi, G. Piccini, A. Pietropaolo, F. Pietrucci, S. Pipolo, D. Provasi, D. Quigley, P. Raiteri, S. Raniolo, J. Rydzewski, M. Salvalaglio, G. Cesare Sosso, V. Spiwok, J. Šponer, D.W.H. Swenson, P. Tiwary, O. Valsson, M. Vendruscolo, G.A. Voth, and A. White) “A community effort to promote transparency and reproducibility in enhanced molecular simulations” Nat. Methods 16 8 670-673 (2019) [ https://doi.org/10.1038/s41592-019-0506-8 ]
53. M.W. Lee, E.Y. Lee, A.L. Ferguson, and G.C.L. Wong “Machine learning antimicrobial peptide sequences: Some surprising variations on the theme of amphiphilic assembly” Curr. Opin. Colloid Interface Sci. 38 204-213 (2018) [ https://doi.org/10.1016/j.cocis.2018.11.003 ]
40. J. Wang and A.L. Ferguson* “Nonlinear machine learning in simulations of soft and biological materials” Mol. Sim. 44 13-14 1090-1107 (2018) [ http://dx.doi.org/10.1080/08927022.2017.1400164 ]
→ Invited review article for the “Free Energy Simulations” special issue
37. A.L. Ferguson* “Machine learning and data science in soft materials engineering” J. Phys.: Condens. Matter 30 4 043002 (2017) [ http://dx.doi.org/10.1088/1361-648X/aa98bd ]
→ Invited review article for J. Phys.: Condens. Matter
35. E.Y. Lee, M.W. Lee, B.M. Fulan, A.L. Ferguson*, and G.C.L. Wong “What can machine learning do for antimicrobial peptides, and what can antimicrobial peptides do for machine learning?” Interface Focus 7 20160153 (2017) [ http://dx.doi.org/10.1098/rsfs.2016.0153 ]
→ RSC Interface Focus invited mini-review
6. A.L. Ferguson, A.Z. Panagiotopoulos, I.G. Kevrekidis and P.G. Debenedetti “Nonlinear dimensionality reduction in molecular simulation: The diffusion map approach” Chem. Phys. Lett. Frontiers 509 1 1-11 (2011)
[ http://dx.doi.org/10.1016/j.cplett.2011.04.066 ]
→ Featured as the cover article of Chemical Physics Letters 509 1 (2011)