Unsupervised learning methods for molecular simulation data
Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …
amounts of data produced by atomistic and molecular simulations, in material science, solid …
[HTML][HTML] Enspara: Modeling molecular ensembles with scalable data structures and parallel computing
Markov state models (MSMs) are quantitative models of protein dynamics that are useful for
uncovering the structural fluctuations that proteins undergo, as well as the mechanisms of …
uncovering the structural fluctuations that proteins undergo, as well as the mechanisms of …
Binding free energies of conformationally disordered peptides through extensive sampling and end-point methods
MG Nixon, E Fadda - Protein Self-Assembly: Methods and Protocols, 2019 - Springer
The ability to obtain binding free energies from molecular simulation techniques provides a
valuable support to the interpretation and design of experiments. Among all methods …
valuable support to the interpretation and design of experiments. Among all methods …