About

Andrew Ferguson is a Professor of Molecular Engineering, Vice Dean for Education and Outreach, and Director of Graduate Studies for the PhD in Molecular Engineering (Materials Track) at the Pritzker School of Molecular Engineering at the University of Chicago. He joined the Pritzker School of Molecular Engineering in July 2018 as an associate professor of immunoengineering. Ferguson received an M.Eng. in chemical engineering from Imperial College London in 2005 and a PhD in chemical and biological engineering from Princeton University in 2010. His postdoctoral work was conducted at the Ragon Institute of MGH, Massachusetts Institute of Technology, and Harvard in the Department of Chemical Engineering at MIT from 2010 to 2012. He began his independent career at the University of Illinois at Urbana-Champaign in 2012, where he was promoted to associate professor of materials science and engineering and chemical and biomolecular engineering in 2018. Ferguson’s research focuses on using computation and theory to understand and design self-assembling materials, macromolecular folding, and antiviral therapies. His work in materials science involves applying nonlinear manifold learning to simulations of polymers, peptides, and colloids to determine folding and assembly mechanisms and rational design principles. In virology, he developed a statistical inference procedure to translate viral sequence databases into empirical models of fitness, which are coupled with models of host-pathogen interaction to perform computational design of vaccine immunogens against HIV and hepatitis C virus. His research in enhanced sampling combines tools from dynamical systems theory and nonlinear manifold learning to recover folding landscapes from molecular observables, utilizing deep learning for on-the-fly collective variable identification and accelerated recovery of molecular free energy landscapes in simulations. Ferguson’s lab employs tools from theory, computation, data science, and machine learning to understand macromolecular folding, engineer self-assembling colloids and peptides, and design antiviral therapies.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Nanotechnology
• Mathematics
• Biology
• Data science
• Chemistry
• Computational chemistry
• Algorithm
• Bioinformatics
• Microbiology
• Biotechnology
• Statistical physics
• Physics
• Computational biology
• Computational science

Selected publications

• 100th Anniversary of Macromolecular Science Viewpoint: Data-Driven Protein Design

  ACS Macro Letters · 2021 · 44 citations

  1st authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Data science

  The design of synthetic proteins with the desired function is a long-standing goal in biomolecular science, with broad applications in biochemical engineering, agriculture, medicine, and public health. Rational de novo design and experimental directed evolution have achieved remarkable successes but are challenged by the requirement to find functional "needles" in the vast "haystack" of protein sequence space. Data-driven models for fitness landscapes provide a predictive map between protein sequence and function and can prospectively identify functional candidates for experimental testing to greatly improve the efficiency of this search. This Viewpoint reviews the applications of machine learning and, in particular, deep learning as part of data-driven protein engineering platforms. We highlight recent successes, review promising computational methodologies, and provide an outlook on future challenges and opportunities. The article is written for a broad audience comprising both polymer and protein scientists and computer and data scientists interested in an up-to-date review of recent innovations and opportunities in this rapidly evolving field.

  DOI

• The value of antimicrobial peptides in the age of resistance

  The Lancet Infectious Diseases · 2020 · 1090 citations

  • Computational biology
  • Biology
  • Biotechnology
  DOI

• Machine learning for collective variable discovery and enhanced sampling in biomolecular simulation

  Molecular Physics · 2020 · 148 citations

  Senior authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Computer Science

  Classical molecular dynamics simulates the time evolution of molecular systems through the phase space spanned by the positions and velocities of the constituent atoms. Molecular-level thermodynamic, kinetic, and structural data extracted from the resulting trajectories provide valuable information for the understanding, engineering, and design of biological and molecular materials. The cost of simulating many-body atomic systems makes simulations of large molecules prohibitively expensive, and the high-dimensionality of the resulting trajectories presents a challenge for analysis. Driven by advances in algorithms, hardware, and data availability, there has been a flare of interest in recent years in the applications of machine learning – especially deep learning – to molecular simulation. These techniques have demonstrated great power and flexibility in both extracting mechanistic understanding of the important nonlinear collective variables governing the dynamics of a molecular system, and in furnishing good low-dimensional system representations with which to perform enhanced sampling or develop long-timescale dynamical models. It is the purpose of this article to introduce the key machine learning approaches, describe how they are married with statistical mechanical theory into domain-specific tools, and detail applications of these approaches in understanding and accelerating biomolecular simulation.

  DOI

• Machine learning force fields and coarse-grained variables in molecular\n dynamics: application to materials and biological systems

  Journal of Chemical Theory and Computation · 2020 · 224 citations

  • Computer Science
  • Machine Learning
  • Artificial Intelligence

  Machine learning encompasses a set of tools and algorithms which are now\nbecoming popular in almost all scientific and technological fields. This is\ntrue for molecular dynamics as well, where machine learning offers promises of\nextracting valuable information from the enormous amounts of data generated by\nsimulation of complex systems. We provide here a review of our current\nunderstanding of goals, benefits, and limitations of machine learning\ntechniques for computational studies on atomistic systems, focusing on the\nconstruction of empirical force fields from ab-initio databases and the\ndetermination of reaction coordinates for free energy computation and enhanced\nsampling.\n

  DOI

Recent grants

• DMREF: Collaborative Research: Self-assembled peptide-pi-electron supramolecular polymers for bioinspired energy harvesting, transport and management

  NSF · $537k · 2017–2018

• Latent Space Simulators for the Efficient Estimation of Long-time Molecular Thermodynamics and Kinetics

  NSF · $388k · 2022–2026

• Nonlinear dimensionality reduction and enhanced sampling in molecular simulation using auto-associative neural networks

  NSF · $304k · 2018–2022

• CAREER: Teaching Machines to Design Self-Assembling Materials

  NSF · $360k · 2014–2018

• EAGER: (ST1) Collaborative Research: Exploring the emergence of peptide-based compartments through iterative machine learning, molecular modeling, and cell-free protein synthesis

  NSF · $150k · 2019–2022

Frequent coauthors

• Claire S. Adjiman

  Imperial College London

  1601 shared

• Chris Goodall

  University of Chicago

  1600 shared

• Kristi L. Kiick

  University of Delaware

  1600 shared

• Neil R. Champness

  University of Birmingham

  1600 shared

• Andrew J. deMello

  Institute for Biomedical Engineering

  1600 shared

• Allison Holloway

  Nanjing University

  1600 shared

• Bianca Provost

  Korea Advanced Institute of Science and Technology

  1600 shared

• Robert A. Riggleman

  University of Pennsylvania

  1600 shared

Labs

• Andrew Ferguson LabPI

Education

• Ragon Postdoctoral Fellow, Chemical Engineering

  Massachusetts Institute of Technology

  2012

• PhD, Chemical and Biological Engineering

  Princeton University

  2010

• MEng, Chemical Engineering

  Imperial College London

  2005

Awards & honors

• 2017 UIUC College of Engineering Dean’s Award for Excellence…
• 2016 AIChE CoMSEF Young Investigator Award for Modeling and…
• 2015 ACS OpenEye Outstanding Junior Faculty Award
• 2014 NSF Career Award
• 2014 ACS PRF Doctoral New Investigator

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