– E. Sevgen, J. Moller, A. Lange, J. Parker, S. Quigley, J. Mayer, P. Srivastava, S. Gayatri, D. Hosfield, M. Korshunova, M. Livne, M. Gill, R. Ranganathan, A.B. Costa, and A.L. Ferguson* “ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design” (submitted, 2023) [ https://www.biorxiv.org/content/10.1101/2023.01.23.525232v1 ]

– Y. Wang, H.-J. Jang, M. Topel, S. Dasetty, Y. Liu, M. Ibrahim, J. Van Buren, V. Rozyyev, E. Ouyang, W. Zhuang, H. Pu, S.S. Lee, J.W. Elam, A.L. Ferguson, S.B. Darling, and J. Chen “Reversible ppt-level detection of perfluorooctane sulfonic acid in tap water using field-effect transistor sensors” (submitted, 2024)

– M.S. Jones, K. Shmilovich, and A.L. Ferguson* “Tutorial on Molecular Latent Space Simulators (LSS): Spatially and temporally continuous data-driven surrogate dynamical models of molecular systems” J. Phys. Chem. A (accepted, 2024)

– A. Berlaga, K. Torkelson, A. Seal, J. Pfaendtner, and A.L. Ferguson* “A modular and extensible CHARMM-compatible model for all-atom simulation of polypeptoids” (submitted, 2024) [ https://doi.org/10.48550/arXiv.2409.06103 ]

– J. Wu, S. Dasetty, D. Beckett, Y. Wang, W. Xue, T. Skóra, T.C. Bidone, A.L. Ferguson*, and G.A. Voth “Data-driven equation-free dynamics applied to many-protein complexes: The microtubule tip relaxation” (submitted, 2024) [ https://biorxiv.org/cgi/content/short/2024.10.10.617682v1 ]

– N. Praljak, H. Yeh, M. Moore, M. Socolich, R. Ranganathan, and A.L. Ferguson* “Natural language prompts guide the design of novel functional protein sequences” NeurIPS 2024 Workshop AIDrugX (accepted, 2024) [ https://openreview.net/forum?id=L1MyyRCAjX&noteId=L1MyyRCAjX ]

125.   S. Chen, E. Valenton, G.M. Rotskoff, A.L. Ferguson*, S.A. Rice, and N.F. Scherer “Power dissipation and entropy production rate of high-dimensional optical matter systems” Phys. Rev. E 110 044109 (2024) [ http://dx.doi.org/10.1103/PhysRevE.110.044109 ]

124. X. Lian, N. Praljak, S. K. Subramanian, S. Wasinger, R. Ranganathan, and A.L. Ferguson* “Deep-learning-based design of synthetic orthologs of SH3 signaling domains” Cell Systems 15 8 P725-737.E5 (2024) [ https://doi.org/10.1016/j.cels.2024.07.005 ]

→ Featured in commentary X. Fu “How deep can we decipher protein evolution with deep learning models” Patterns 5 8 101043 (2024) [ https://doi.org/10.1016/j.patter.2024.101043 ]

123. S. Chen, A.L. Ferguson*, S.A. Rice, and N.F. Scherer “Raman effect-inspired insights into collective fluctuation mode-dependent light scattering of optical matter systems” J. Phys. Chem. C 128 30 12582-12592 (2024) [ https://doi.org/10.1021/acs.jpcc.4c03328 ]

122. A.L. Ferguson* and J. Pfaendtner “Special Issue Preface: Virtual Special Issue on Machine Learning in Physical Chemistry Volume 2” J. Phys. Chem B 128 27 6435-6438 (2024) [ https://doi.org/10.1021/acs.jpcb.4c03823 ]

121. R. Zheng, M. Zhao, J.S. Du, T.R. Sudarshan, Y. Zhou, A.K. Paravastu, J.J. De Yoreo, A.L. Ferguson, and C.-L. Chen “Assembly of short amphiphilic peptoids into nanohelices with controllable supramolecular chirality” Nat. Commun. 15 3264 (2024) [ https://doi.org/10.1038/s41467-024-46839-y ]

120. S. Dasetty, T.C. Bidone, and A.L. Ferguson* “Data-driven prediction of α_IIbβ₃ integrin activation paths using manifold learning and deep generative modeling” Biophys. J. 123 17 2716-2729 (2024) [ https://doi.org/10.1016/j.bpj.2023.12.009 ]

119. N.S.M. Herringer, S. Dasetty, D. Gandhi, J. Lee, and A.L. Ferguson* “Permutationally Invariant Networks for Enhanced Sampling (PINES): Discovery of multi-molecular and solvent-inclusive collective variables” J. Chem. Theory Comput. 20 178-198 (2024) [ https://doi.org/10.1021/acs.jctc.3c00923 ]

118. T.E. Gartner III, A.L. Ferguson, and Pablo G. Debenedetti “Data-driven molecular design and simulation in modern chemical engineering” Nat. Chem. Eng. 1 6-9 (2024) [ https://doi.org/10.1038/s44286-023-00010-4 ]

117. B. Ashwood, M.S. Jones, Y. Lee, J.R. Sachleben, A.L. Ferguson*, and A. Tokmakoff “Molecular insight into how the position of an abasic site modifies DNA duplex stability and dynamics” Biophys. J. 123 2 118-133 (2024) [ https://doi.org/10.1016/j.bpj.2023.11.022 ]

116. P.F. Zubieta Rico, L. Schneider, G. Perez-Lemus, R. Alessandri, S. Dasetty, C.A. Menéndez, Y. Wu, Y. Jin, Y. Xu, T. Nguyen, J. Parker, A.L. Ferguson, J. Whitmer, S. Varner, and J.J. de Pablo “PySAGES: flexible, advanced sampling methods accelerated with GPUs” npj Comput. Mater. 10 35 (2024) [ https://doi.org/10.1038/s41524-023-01189-z ]

115. B. Ashwood, M.S. Jones, A. Radakovic, S. Khanna, Y. Lee, J.R. Sachleben, J.W. Szostak, A.L. Ferguson, and A. Tokmakoff “Thermodynamics and kinetics of DNA and RNA dinucleotide hybridization to gaps and overhangs” Biophys. J. 122 3323-3339 (2023) [ https://doi.org/10.1016/j.bpj.2023.07.009 ]

114. S. Dasetty, M. Topel, Y. Tang, Y. Wang, E. Jonas, S.B. Darling, J. Chen, and A.L. Ferguson* “Data-driven discovery of linear molecular probes with optimal selective affinity for PFAS in water” J. Chem. Eng. Data 68 3148-3161 (2023) [ https://doi.org/10.1021/acs.jced.3c00404 ]

→ Invited article for “Machine Learning for Thermophysical Properties” virtual special issue

113. N. Praljak, X. Lian, R. Ranganathan, and A.L. Ferguson* “ProtWave-VAE: Integrating autoregressive sampling with latent-based inference for data-driven protein design” ACS Synth. Biol. 12 3544-3561 (2023) [ https://doi.org/10.1021/acssynbio.3c00261 ]

→ Invited article for “AI for Syntheric Biology” special issue

112. Y. Tang, J.Y. Kim, C. KM IP, A. Bahmani, Q. Chen, M.G. Rosenberger, A.P. Esser-Kahn, and A.L. Ferguson* “Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening” Chem. Sci. 14 12747-12766 (2023) [ https://doi.org/10.1039/D3SC03613H ]

111. M.S. Jones, K. Shmilovich and A.L. Ferguson* “DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα protein traces” J. Chem. Theory Comput. 19 7908−7923 (2023) [ https://doi.org/10.1021/acs.jctc.3c00840 ]

110. C. Adjiman and A.L. Ferguson* “New Editor-in-Chief and Deputy Editor-in-Chief for MSDE: reflections and vision” 8 1095-1096 (2023) [ http://doi.org/10.1039/d3me90026f ]

109.  E.R. Crabtree, J.M. Bello-Rivas, A.L. Ferguson, and I.G. Kevrekidis “GANs and closures: Micro-macro consistency in multiscale modeling” Multiscale Modeling and Simulation 21 3 1122-1146 (2023) [ https://doi.org/10.1137/22M151783 ]

108. S. Alamdari, K. Torkelson, X. Wang, C.-L. Chen, A.L. Ferguson, and J. Pfaendtner “Thermodynamic basis for stabilization of helical peptoids by chiral sidechains” J. Phys. Chem. B 127 6171-6183 (2023) [ https://doi.org/10.1021/acs.jpcb.3c01913 ]

107. M.S. Jones, Z.A. McDargh, R.P. Wiewiora, J.A. Izaguirre, H. Xu, and A.L. Ferguson* “Molecular latent space simulators for distributed and multi-molecular trajectories” J. Phys. Chem. A 127 5470-5490 (2023) [ https://doi.org/10.1021/acs.jpca.3c01362 ]

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. 9 1200-1212 (2023) [ https://doi.org/10.1021/acscentsci.3c00178 ]

→ Selected for supplementary cover art of ACS Cent. Sci. vol. 9, issue 6 (June 28, 2023)

105. M. Zhao, S. Zhang, R. Zheng, S. Alamdari, C.J. Mundy, J. Pfaendtner, L.D. Pozzo, C.-L. Chen, J. DeYoreo, and A.L. Ferguson* “Computational and experimental determination of the properties, structure, and stability of peptoid nanosheets and nanotubes” Biomacromolecules 24 6 2618-2632 (2023) [ https://doi.org/10.1021/acs.biomac.3c00107 ]

104. J.Y. Kim, M.G. Rosenberger, S. Chen, C. IP, A. Bahmani, Q. Chen, J. Shen, Y. Tang, A. Wang, E. Kenna, M. Son, S. Tay, A.L. Ferguson, and A.P. Esser-Kahn “Discovery of new states of immunomodulation for vaccine adjuvants via high throughput screening: Expanding innate responses to PRRs” ACS Cent. Sci. 9 427-439 (2023) [ https://doi.org/10.1021/acscentsci.2c01351 ]

103. K. Shmilovich and A.L. Ferguson* “Girsanov Reweighting Enhanced Sampling Technique (GREST): On-the-fly data-driven discovery of and enhanced sampling in slow collective variables” J. Phys. Chem. A 127 15 3497-3517 (2023) [ https://doi.org/10.1021/acs.jpca.3c00505 ]

→ Invited article for Pablo G. Debenedetti Festschrift virtual special issue

102. B. Ashwood, M.S. Jones, A.L. Ferguson, and A. Tokmakoff “Disruption of energetic and dynamic base pairing cooperativity in DNA duplexes by an abasic site” Proc. Natl. Acad. Sci. USA 120 14 e2219124120 (2023) [ https://doi.org/10.1073/pnas.2219124120 ]

101. J. Pfaendtner and A.L. Ferguson* “Characteristics of Impactful Machine Learning Contributions to The Journal of Physical Chemistry” J. Phys. Chem. A 127 2 415–417(2022) [ https://dx.doi.org/10.1021/acs.jpca.2c08702 ] J. Phys. Chem. B 127 2 427–429(2022) [ https://dx.doi.org/10.1021/acs.jpcb.2c08703 ] J. Phys. Chem. C 127 2 849–851(2022) [ https://dx.doi.org/10.1021/acs.jpcc.2c08704 ]

100. M. Topel, A. Ejaz, A.H. Squires, and A.L. Ferguson* “Learned reconstruction of protein folding trajectories from noisy single-molecule time series” J. Chem. Theory Comput. 19 4654-4667 (2023) [ http://dx.doi.org/10.1021/acs.jctc.2c00920 ]

→ Invited article for Machine Learning for Molecular Simulation special issue

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 ]

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 ]

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 ]

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 ]

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

89. M. Zhao, K.J. Lachowski, S. Alamdari, J. Sampath, P. Mu, C.J. Mundy, J. Pfaendtner, C.-L. Chen, L.D. Pozzo, and A.L. Ferguson* “Hierarchical self-assembly pathways of polypeptoid helices and sheets” Biomacromolecules 23 3 992-1008 (2022) [ https://doi.org/10.1021/acs.biomac.1c01385 ]

88. B. Sharma, Y. Ma, H.L. Hiraki, B.M. Baker, A.L. Ferguson, and A.P. Liu “Facile formation of giant elastin-like polypeptide vesicles as synthetic cells” Chem. Commun. 57 13202-13205 (2021) [ https://doi.org/10.1039/D1CC05579H ]

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 ]

86. S.S. Panda, K. Shmilovich, S.M. Herringer, N.J. Marin, A.L. Ferguson, and J.D. Tovar “Computationally guided tuning of peptide-conjugated perylene diimide self-assembly” Langmuir 37 28 8594-8606 (2021) [ https://doi.org/10.1021/acs.langmuir.1c01213 ]

85. W. Alvarado, J. Moller, A.L. Ferguson*, and J.J. de Pablo “Tetranucleosome interactions drive chromatin folding” ACS Cent. Sci. 7 6 1019–1027 (2021) [ https://doi.org/10.1021/acscentsci.1c00085 ]

→ Selected for supplementary cover art of ACS Cent. Sci. vol. 7, issue 6 (June 23, 2021)

84. S. Chen, C.W. Peterson, J.A. Parker, S.A. Rice, A.L. Ferguson*, and N.F. Scherer “Data-driven reaction coordinate discovery in overdamped and non-conservative systems: Application to optical matter structural isomerization” Nat. Commun. 12 2548 (2021) [ https://doi.org/10.1038/s41467-021-22794-w ]

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 ]

82. Y. Ma, J. Aulicino, and A.L. Ferguson* “Inverse design of self-assembling diamond photonic lattices from anisotropic colloidal clusters” J. Phys. Chem B 125 9 2398-2410 (2021) [ https://dx.doi.org/10.1021/acs.jpcb.0c08723 ]

→ Invited article for “Carol K. Hall Festschrift”

81. Y. Yang; H. Ying, Z. Li, J. Wang, Y. Chen. B. Luo, D.L. Gray, A.L. Ferguson, Q. Chen, Y. Z, and J. Cheng “Near quantitative synthesis of urea macrocycles enabled by bulky N-substituent” Nat. Commun. 12 1572 (2021) [ https://doi.org/10.1038/s41467-021-21678-3 ]

80. Y. Xia, Z. Song, T. Xue, S. Wei, L. Zhu, Z. Tan, Y. Yang, H. Fu, Y. Jiang, Y. Lin, Y. Lu, A.L. Ferguson*, and J. Cheng “Accelerated polymerization of N-carboxyanhydrides catalyzed by crown ether” Nat. Commun. 12 732 (2021) [ https://doi.org/10.1038/s41467-020-20724-w ]

→ Selected for Editors’ Highlight of 50 best recent papers published in organic chemistry and chemical biology

79. C.H. Fry, B. Peters, and A.L. Ferguson “Pushing and pulling: A dual pH triggered heme peptide assembly” J. Phys. Chem. B 125 5 1317-1330 (2021) [ https://dx.doi.org/10.1021/acs.jpcb.0c07713 ]

78. E.Y. Lee, L.C. Chan, H. Wang, J. Lieng, M. Hung, Y. Srinivasan, J. Wang, J. Waschek, A.L. Ferguson, K.-F. Lee, N.Y. Yount, M.R. Yeaman, and G.C.L. Wong “Mood-modulating neuropeptide PACAP is potently induced during infection” Proc. Natl. Acad. Sci. USA 118 1 e1917623117 (2020) [ https://doi.org/10.1073/pnas.1917623117 ]

→ Highlighted in an accompanying commentary article:  M. Zasloff “An ancient neuropeptide defends the brain against infection” PNAS 118 5 e2023990118 (2020)

77. A.L. Ferguson*, J. Hachmann, T.F. Miller, and J. Pfaendtner “Editorial: The Journal of Physical Chemistry A/B/C Virtual Special Issue on Machine Learning in Physical Chemistry” J. Phys. Chem B 124 9767−9772 (2020) [ https://dx.doi.org/10.1021/acs.jpcb.0c09206 ]

→ Invited editorial for the ” Virtual Special Issue on Machine Learning in Physical Chemistry”

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 ]

75. M. Topel and A.L. Ferguson* “Reconstruction of protein structures from single molecule time series” J. Chem. Phys. 153 194102 (2020) [ https://doi.org/10.1063/5.0024732 ]

→ Invited submission to the “2020 JCP Emerging Investigators in Science Collection”

73. M. Zhao, J. Sampath, S. Alamdari, G. Shen, C.-L. Chen, C.J. Mundy, J. Pfaendtner, and A.L. Ferguson* “MARTINI-compatible coarse-grained model for the mesoscale simulation of peptoids” J. Phys. Chem. B 124 36 7745–7764 (2020) [ http://dx.doi.org/10.1021/acs.jpcb.0c04567 ]

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 ]

71. S. Panda, K. Shmilovich, A.L. Ferguson, and J.D. Tovar “Computationally guided tuning of amino acid configuration influences the chiroptical properties of supramolecular peptide-π-peptide nanostructures” Langmuir 36 24 6782–6792 (2020) [ https://dx.doi.org/10.1021/acs.langmuir.0c00961 ]

70. B.L. Peters, J. Deng, and A.L. Ferguson* “Free-energy calculations of the functional selectivity of 5-HT_2B G protein coupled receptor” PLoS ONE 15 12 e0243313 (2020) [ https://doi.org/10.1371/journal.pone.0243313 ]

69. E. Jira, K. Shmilovich, T. Kale, A.L. Ferguson, J.D. Tovar, and C. Schroeder “Effect of core oligomer length on the phase behavior and assembly of π-conjugated peptides” ACS Appl. Mater. Interfaces 12 20722-20732 (2020) [ https://dx.doi.org/10.1021/acsami.0c02095 ]

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)

67. M.R. Shirts and A.L. Ferguson* “Statistically optimal continuous potentials of mean force from umbrella sampling and multistate reweighting” J. Chem. Theory Comput. 16 7 4107–4125 (2020) [ https://dx.doi.org/10.1021/acs.jctc.0c00077 ]

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 ]

64. B.A. Thurston, E.P. Shapera, J.D. Tovar, A. Schleife, and A.L. Ferguson* “Revealing the sequence-structure-electronic property relation of self-assembling π-conjugated oligopeptides by molecular and quantum mechanical modeling” Langmuir 35 47 15221-15231 (2019) [ https://doi.org/10.1021/acs.langmuir.9b02593 ]

63. S. Panda, K. Shmilovich, A.L. Ferguson*, and J.D. Tovar “Controlling supramolecular chirality in peptide-π-peptide networks by variation of alkyl spacer length” Langmuir 35 43 14060-14073 (2019) [ https://doi.org/10.1021/acs.langmuir.9b02683 ]

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 ]

61. H. Sidky, W. Chen, and A.L. Ferguson* “High-resolution Markov state models for the dynamics of Trp-cage miniprotein constructed over slow folding modes identified by state-free reversible VAMPnets” J. Phys. Chem. B 123 38 7999-8009 (2019) [ http://dx.doi.org/10.1021/acs.jpcb.9b05578 ]

60. W. Chen, H. Sidky, and A.L. Ferguson* “Capabilities and limitations of time-lagged autoencoders for slow mode discovery in dynamical systems” J. Chem. Phys. 151 064123 (2019) [ https://doi.org/10.1063/1.5112048 ]

59. Y. Ma and A.L. Ferguson* “Inverse design of self-assembling colloidal crystals with omnidirectional photonic bandgaps” Soft Matter 15 8808-8826 (2019) [ https://doi.org/10.1039/C9SM01500K ]

58. A.L. Ferguson*, T. Mueller, S. Rajasekaran, and B.J. Reich “Conference report: 2018 materials and data science hackathon (MATDAT18)” Mol. Syst. Des. Eng. 4 462-468 (2019) [ https://doi.org/10.1039/c9me90018g ]

57. J. Chen, J. Wang, K. Li, Y. Wang, M. Gruebele, A.L. Ferguson, and S.C. Zimmerman “Polymeric ‘clickase’ accelerates the copper click reaction of small molecules, proteins, and cells” J. Am. Chem. Soc. 141 9693-9700 (2019) [ https://doi.org/10.1021/jacs.9b04181 ]

56. W. Chen, H. Sidky, and A.L. Ferguson* “Nonlinear discovery of slow molecular modes using state-free reversible VAMPnets” J. Chem. Phys. 150 214114 (2019) [ https://doi.org/10.1063/1.5092521 ]

→ Selected as J. Chem. Phys. “Editor’s Pick”

55. Z. Song, H. Fu, J. Wang, J. Hui, T. Xue, L.A. Pacheco, H. Yan, R. Baumgartner, Z. Wang, Y. Xia, X. Wang, L. Yin, C. Chen, J. Rodríguez-López, A.L. Ferguson, Y. Lin, and J. Cheng “Synthesis of polypeptides via bio inspired polymerization of in situ purified N-carboxyanhydrides” Proc. Natl. Acad. Sci. USA 116 22 10658-10663 (2019) [ https://doi.org/10.1073/pnas.1901442116 ]

54. A.W. Long and A.L. Ferguson* “Landmark diffusion maps (L-dMaps): Accelerated manifold learning out-of-sample extension” Appl. Comput. Harmon. Anal. 47 1 190-211 (2019) [ http://dx.doi.org/10.1016/j.acha.2017.08.004 ]

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 ]

52. J. Wang and A.L. Ferguson* “Recovery of protein folding funnels from single-molecule time series by delay embeddings and manifold learning” J. Phys. Chem. B 122 50 11931–11952(2018) [ https://doi.org/10.1021/acs.jpcb.8b08800 ]

→ Invited submission to the “Deciphering Molecular Complexity in Dynamics and Kinetics from the Single Biomolecule to Single Cell Levels” special issue

51. A.L. Ferguson* “Editorial: ACS Central Science Virtual Issue on Machine Learning” ACS Cent. Sci. 4 8 938-941 (2018) [ http://dx.doi.org/10.1021/acscentsci.8b00528 ]

→ Invited editorial for the ” Virtual Issue on Machine Learning”

50. G.R. Hart and A.L. Ferguson* “Computational design of hepatitis C virus immunogens from host-pathogen dynamics over empirical viral fitness landscapes” Physical Biology 16 016004 (2018) [ https://doi.org/10.1088/1478-3975/aaeec0 ]

49. J. Chen, J. Wang, Y. Bai, K. Li, E.S. Garcia, A.L. Ferguson, and S.C. Zimmerman “Enzyme-like click catalysis by a copper-containing single-chain organic nanoparticle” J. Am. Chem. Soc. 140 42 13695-13702 (2018) [ https://doi.org/10.1021/jacs.8b06875 ]

48. R.A. Mansbach and A.L. Ferguson* “A patchy particle model of the hierarchical self-assembly of π-conjugated optoelectronic peptides” J. Phys. Chem. B 122 44 10219-10236 (2018) [ https://doi.org/10.1021/acs.jpcb.8b05781 ]

47. B.A. Thurston and A.L. Ferguson* “Machine learning and molecular design of self-assembling π-conjugated oligopeptides” Mol. Sim. 44 11 930-945 (2018) [ https://doi.org/10.1080/08927022.2018.1469754 ]

46. J. Wang, M. Gayatri, and A.L. Ferguson* “Coarse-grained molecular simulation and nonlinear manifold learning of archipelago asphaltene aggregation and folding” J. Phys. Chem. B 122 25 6627-6647 (2018) [ https://doi.org/10.1021/acs.jpcb.8b01634 ]

45. L. Valverde, B.A. Thurston, A.L. Ferguson, and W.L. Wilson “Evidence for prenucleated fibrilogenesis of acid-mediated self-assembling oligopeptides via molecular simulation and fluorescence correlation spectroscopy” Langmuir 34 25 7346-7354 (2018) [ https://doi.org/10.1021/acs.langmuir.8b00312 ]

44. W. Chen, A.R. Tan, and A.L. Ferguson* “Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design” J. Chem. Phys. 149 072312 (2018) [ https://doi.org/10.1063/1.5023804 ]

→ Invited submission to the “Enhanced Sampling for Molecular Simulations” issue

43. W. Chen and A.L. Ferguson* “Molecular enhanced sampling with autoencoders: On-the-fly nonlinear collective variable discovery and accelerated free energy landscape exploration” J. Chem. Theory Comput. 39 25 2079-2102 (2018) [ https://doi.org/10.1002/jcc.25520 ]

→ Top 10% or most downloaded papers in 12 months following online publication for period Jan 2018 – Dec 2019

42. A.L. Ferguson* and J. Hachmann “Machine learning and data science in materials design: a themed collection” Mol. Syst. Des. Eng. 3 429-430 (2018) [ https://doi.org/10.1039/C8ME90007H ]

→ Editorial for invited themed collection “Machine Learning and Data Science in Materials Design”

41. J. Wang and A.L. Ferguson “A study of the morphology, dynamics, and folding pathways of ring polymers with supramolecular topological constraints using molecular simulation and nonlinear manifold learning” Macromolecules 51 2 598-616 (2018) [ http://dx.doi.org/10.1021/acs.macromol.7b01684 ]

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

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 for best paper published by the Institution from an author within 10 years of their PhD

38. E.Y. Lee, G.C.L. Wong, and A.L. Ferguson* “Machine learning-enabled discovery and design of membrane-active peptides” Bioorg. Med. Chem. 26 10 2708-2718 (2018) [ http://dx.doi.org/10.1016/j.bmc.2017.07.012 ]

→ Invited mini-review for “Peptide Therapeutics” symposium-in-print

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 J. Phys.: Condens. Matter review article

36. M.W. Lee, E.Y. Lee, G.H. Lai, N.W. Kennedy, A.E. Posey, W. Xian, A.L. Ferguson, R.B. Hill, and G.C.L. Wong “Molecular motor Dnm1 synergistically induces membrane curvature to facilitate mitochondrial fission” ACS Cent. Sci. 3 11 1156-1167 (2017) [ http://dx.doi.org/10.1021/acscentsci.7b00338 ]

→ Featured as the cover article of ACS Central Science

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

34. Z. Song, R.A. Mansbach, R. Baumgartner, K.-C. Shih, H. He, N. Zheng, X. Ba, Y. Huang, D. Mani, Y. Lin, M.-P. Nieh, A.L. Ferguson*, L. Yin, and J. Cheng “Modulation of polypeptide conformation through donor-acceptor transformation of side-chain hydrogen bonding ligands” Nat. Commun. 92 8 1-8 (2017) [ http://dx.doi.org/10.1038/s41467-017-00079-5 ]

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 ]

→ Featured work in Soft Matter promotional flyer

32. R.A. Mansbach and A.L. Ferguson* “Control of the hierarchical assembly of π-conjugated optoelectronic peptides by pH and flow” Org. Biomol. Chem. 15 26 5484-5502 (2017) [ http://dx.doi.org/10.1039/C7OB00923B ]

→ Invited submission for “Peptide Materials” special issue
→ Selected as 2017 HOT Article in Organic and Biomolecular Chemistry
→ Featured as the cover article of Organic and Biomolecular Chemistry 15 26 (2017)

31. J. Wang, M. Gayatri, and A.L. Ferguson* “Mesoscale simulation and machine learning of asphaltene aggregation phase behavior and molecular assembly landscapes” J. Phys. Chem. B 121 18 4923-4944 (2017) [ http://dx.doi.org/10.1021/acs.jpcb.7b02574 ]

30. A.L. Ferguson* “BayesWHAM: A Bayesian approach for free energy estimation, reweighting, and uncertainty quantification in the weighted histogram analysis method” J. Comput. Chem. 38 18 1583-1605 (2017) [ http://dx.doi.org/10.1002/jcc.24800 ]

29. R.A. Mansbach and A.L. Ferguson* “Coarse-grained molecular simulation of the hierarchical self-assembly of π-conjugated optoelectronic peptides” J. Phys. Chem. B 121 7 1684–1706 (2017) [ http://dx.doi.org/10.1021/acs.jpcb.6b10165 ]

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 ]

27. C.D. Allen, M.Y. Chen, A.Y. Trick, D. Thanh Le, A.L. Ferguson*, and A.J. Link “Thermal unthreading of the lasso peptides astexin-2 and astexin-3” ACS Chem. Biol. 11 11 3043-3051 (2016) [ http://dx.doi.org/10.1021/acschembio.6b00588 ]

26. J. Wang and A.L. Ferguson* “Mesoscale simulation of asphaltene aggregation” J. Phys. Chem. B 120 32 8016-8035 (2016) [ http://dx.doi.org/10.1021/acs.jpcb.6b05925 ]

25. A.W. Long, C.L. Phillips, E. Jankowski, and A.L. Ferguson* “Nonlinear machine learning and design of reconfigurable digital colloids” Soft Matter 12 7119-7135 (2016) [ http://dx.doi.org/10.1039/C6SM01156J ]

24. R.A. Mansbach, A.L. Ferguson, K.A. Kilian, J.A. Krogstad, C. Leal, A. Schleife, D.R. Trinkle, M. West, and G.L. Herman “Reforming an undergraduate materials science curriculum with computational modules” J. Mater. Educ. 38 3-4 161-174 (2016)

23. J. Hu and A.L. Ferguson* “Global graph matching using diffusion maps” Intelligent Data Analysis 20 3 637-654 (2016) [ http://dx.doi.org/10.3233/IDA-160824 ]

22. J. Wang and A.L. Ferguson* “Nonlinear reconstruction of single-molecule free-energy surfaces from univariate time series” Phys. Rev. E 93 032412 (2016) [ http://link.aps.org/doi/10.1103/PhysRevE.93.032412 ]

21. B.A. Thurston, J.D. Tovar, and A.L. Ferguson* “Thermodynamics, morphology, and kinetics of early-stage self-assembly of π-conjugated oligopeptides” Mol. Sim. 42 12 955-975 (2016) [ http://dx.doi.org/10.1080/08927022.2015.1125997 ]

20. G.R. Hart and A.L. Ferguson* “Empirical fitness models for hepatitis C virus immunogen design” Phys. Biol. 12 066006 (2015) [ http://dx.doi.org/10.1088/1478-3975/12/6/066006 ]

19. M. Xiong, M.W. Lee, R. Mansbach, Z. Song, Y. Bao, R.M. Peek Jr., C. Yao, L.-F. Chen, A.L. Ferguson*, G.C.L. Wong, and J. Cheng “Helical antimicrobial polypeptides with radial amphiphilicity” Proc. Natl. Acad. Sci. USA 112 43 13155-13160 (2015) [ http://dx.doi.org/10.1073/pnas.1507893112 ]

18. A.W. Long, J. Zhang, S. Granick, and A.L. Ferguson* “Machine learning assembly landscapes from particle tracking data” Soft Matter 11 8141-8153 (2015) [ http://dx.doi.org/10.1039/C5SM01981H ]

17. R.A. Mansbach and A.L. Ferguson* “Machine learning of single molecule free energy surfaces and the impact of chemistry and environment upon structure and dynamics” J. Chem. Phys. 142 105101 (2015) [ http://dx.doi.org/10.1063/1.4914144 ]

→ Ranked as one of the most read Biological Molecules and Networks articles of the year

16. G.R. Hart and A.L. Ferguson* “Error catastrophe and phase transition in the empirical fitness landscape of HIV” Phys. Rev. E 91 032705 (2015) [ http://dx.doi.org/10.1103/PhysRevE.91.032705 ]

15. L. Tang, X. Yang, I. Chaudhury, C. Yao, Q. Yin, Q. Zhou, M. Kwon, L.W. Dobrucki, L.B. Borst, S. Lezmi, W.G. Helferich, A.L. Ferguson*, T.M. Fan and J. Cheng “Investigating the optimal size of anticancer nanomedicine” Proc. Natl. Acad. Sci. USA 111 (43) 15344-15349 (2014) [ http://www.dx.doi.org/10.1073/pnas.1411499111 ]

14. B.D. Wall, Y. Zhou, S. Mei, H.A.M. Ardoña, A.L. Ferguson and J.D. Tovar “Variation of formal hydrogen bonding networks within electronically delocalized pi-conjugated oligopeptide nanostructures” Langmuir 30 (38) 11375–11385 (2014) [ http://www.dx.doi.org/10.1021/la501999g ]

13. J.K. Mann, J.P. Barton, A.L. Ferguson, S. Omarjee, B.D. Walker, A.K. Chakraborty and T. Ndung’u “The fitness landscape of HIV-1 gag: Advanced modeling approaches and validation of model predictions by in vitro testing” PLOS Comput. Biol. 10 8 e1003776 (2014) [ http://dx.doi.org/10.1371/journal.pcbi.1003776 ]

12. B.D. Wall, A.E. Zacca, A.M. Sanders, W.L. Wilson, A.L. Ferguson and J.D. Tovar “Supramolecular polymorphism: Tunable electronic interactions within pi-conjugated peptide nanostructures dictated by primary amino acid sequence” Langmuir 30 20 5946-5956 (2014) [ http://dx.doi.org/10.1021/la500222y ]

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 ]

10. K. Shekhar, C.F. Ruberman, A.L. Ferguson, J.P. Barton, M. Kardar, A.K. Chakraborty “Spin models inferred from patient-derived viral sequence data faithfully describe HIV fitness landscapes” Phys. Rev. E 88 062705 (2013) [ http://dx.doi.org/10.1103/PhysRevE.88.062705 ]

9. A.L. Ferguson, E. Falkowska, L.M. Walker, M.S. Seaman, D.R. Burton and A.K. Chakraborty “Computational prediction of broadly neutralizing HIV-1 antibody epitopes from neutralization activity data” PLOS ONE 8 12 e80562 (2013) [ http://dx.doi.org/10.1371/journal.pone.0080562 ]

8. A.L. Ferguson, J.K. Mann, S. Omarjee, T. Ndung’u, B.D. Walker and A.K. Chakraborty “Translating HIV sequences into quantitative fitness landscapes predicts viral vulnerabilities for rational immunogen design” Immunity 38 606-617 (2013) [ http://dx.doi.org/10.1016/j.immuni.2012.11.022 ]

→ Highlighted in an accompanying commentary article:  N. Goonetilleke and A.J. McMichael “HIV-1 vaccines: Let’s get physical” Immunity 38 410-413 (2013)
→ Editorial selection as feature in Cell “Select” series on antiviral strategies in Cell 153 4 (2013)

7. A.L. Ferguson*, N. Giovambattista, P.J. Rossky, A.Z. Panagiotopoulos and P.G. Debenedetti “A computational investigation of the phase behavior and capillary sublimation of water confined between nanoscale hydrophobic plates” J. Chem. Phys. 137 144501 (2012) [ http://dx.doi.org/10.1063/1.4755750 ]

→ Featured as the cover article of Journal of Chemical Physics 137 (2012)
→ Most read regular Journal of Chemical Physics article in October 2012
→ Selected as a 2012 Journal of Chemical Physics Editor’s Choice article

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)

5. A.L. Ferguson*, A.Z. Panagiotopoulos, P.G. Debenedetti and I.G. Kevrekidis “Integrating diffusion maps with umbrella sampling: Application to alanine dipeptide” J. Chem. Phys. 134 135103 (2011) [ http://dx.doi.org/10.1063/1.3574394 ]

4. A.L. Ferguson, S. Zhang, I. Dikiy, A.Z. Panagiotopoulos, P.G. Debenedetti and A.J. Link “An experimental and computational investigation of lasso formation in microcin J25” Biophys. J. 99 9 3056-3065 (2010) [ http://dx.doi.org/10.1016/j.bpj.2010.08.073 ]

3. A.L. Ferguson, A.Z. Panagiotopoulos, P.G. Debenedetti and I.G. Kevrekidis “Systematic determination of order parameters for chain dynamics using diffusion maps” Proc. Natl. Acad. Sci. USA 107 31 13597-13602 (2010) [ http://dx.doi.org/10.1073/pnas.1003293107 ]

2. A.L. Ferguson, P.G. Debenedetti and A.Z. Panagiotopoulos “Solubility and molecular conformations of n-alkane chains in water” J. Phys. Chem. B 113 18 6405-6414 (2009) [ http://dx.doi.org/10.1021/jp811229q ]

1. E. Guibal, T. Vincent, E. Touraud, S. Colombo, and A.L. Ferguson “Oxidation of hydroquinone to p-benzoquinone catalyzed by Cu(II) supported on chitosan flakes” J. Appl. Polym. Sci. 100 3034-3043 (2006) [ http://dx.doi.org/10.1002/app.23702 ]