The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynamics (MD) simulation. It is still an open question about what is optimal for this choice...
This review describes recent advances by the authors and others on the topic of incorporating experimental data into molecular simulations through maximum entropy methods. Methods which incorporate experimental data improve accuracy in molecular simulation by minimally modifying the thermodynamic ensemble. This is especially important where force fields are approximate, such as when employing coarse-grain models, or where high accuracy is required, such as when attempting to mimic a multiscale self-assembly process. The authors review here the experiment directed simulation (EDS) and experiment directed metadynamics (EDM) methods that allow matching averages and distributions in simulations, respectively. Important system-specific considerations are discussed such as using enhanced sampling simultaneously, the role of pressure, treating uncertainty, and implementations of these methods. Recent examples of EDS and EDM are reviewed including applications to ab initio molecular dynamics of water, incorporating environmental fluctuations inside of a macromolecular protein complex, improving RNA force fields, and the combination of enhanced sampling with minimal biasing to model peptides.
In this work, we investigate the question: do code-generating large language models know chemistry? Our results indicate, mostly yes. To evaluate this, we introduce an expandable framework for evaluating chemistry...
Chemists can be skeptical in using deep learning (DL)
in decision
making, due to the lack of interpretability in “black-box”
models. Explainable artificial intelligence (XAI) is a branch of artificial
intelligence (AI) which addresses this drawback by providing tools
to interpret DL models and their predictions. We review the principles
of XAI in the domain of chemistry and emerging methods for creating
and evaluating explanations. Then, we focus on methods developed by
our group and their applications in predicting solubility, blood–brain
barrier permeability, and the scent of molecules. We show that XAI
methods like chemical counterfactuals and descriptor explanations
can explain DL predictions while giving insight into structure–property
relationships. Finally, we discuss how a two-step process of developing
a black-box model and explaining predictions can uncover structure–property
relationships.
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