Mingfei’s terrific new single-author review article summarizes recent advances in the hierarchical self-assembly of polypeptoid-based nanomaterials. Terrific work, Mingfei!
Our collaborative study with Gerard Wong and Michael Yeaman on the identification of the PACAP neuropeptide as an ancient antimicrobial protector of the brain and published in PNAS was highlighted …
Nikša Praljak joins the Ferguson Lab and Ranganathan Lab as a co-advised graduate research assistant. Nikša will use deep learning techniques to perform data-driven protein design. Welcome, Nikša!
Kate Johnson joins the Ferguson Lab and Chevrier Lab as a co-advised graduate research assistant. Kate’s work will use machine learning tools to understand immunological signaling pathways within cells and …
Max’s paper detailing the use of deep learning, dynamical systems, theory, and statistical thermodynamics for the reconstruction of atomistic folding trajectories from experimentally-measurable time series comes out in the J. …
Check out the new Virtual Special Issue on Machine Learning in Physical Chemistry in J. Phys. Chem. A/B/C co-edited with Jim Pfaendtner, Tom Miller, and Johannes Hachmann.
Hythem and Wei’s new Chem. Sci. paper reports the use of three back-to-back specialized deep learning networks to perform continuous atomistic simulations at 6 orders of magnitude lower cost than …
Prof. Ferguson received a 2020 Machine Learning in the Chemical Sciences and Engineering Award, a new honor presented by the Camille and Henry Dreyfus Foundation that recognizes projects with potential …
Kirill’s paper using coarse-grained molecular simulation, deep representational learning, Gaussian process regression, and Bayesian optimization was selected to be featured on the front cover of J. Phys. Chem. B and …
Walter Alvarado joins the Ferguson Lab and de Pablo Lab as a co-advised graduate research assistant. Walter will work on molecular simulation and machine learning of nucleosome dynamics and assembly. …