Journal of chemical theory and computation
35179897
The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has b...
Journal of chemical information and modeling
35352898
Conformational sampling of protein structures is essential for understanding biochemical functions and for predicting thermodynamic properties such as free energies. Where previous approaches rely on sequential sampling procedures, recent development...
Journal of chemical theory and computation
35290063
Transferable high dimensional neural network potentials (HDNNPs) have shown great promise as an avenue to increase the accuracy and domain of applicability of existing atomistic force fields for organic systems relevant to life science. We have previ...
Atomistic simulations using accurate energy functions can provide molecular-level insight into functional motions of molecules in the gas and in the condensed phase. This Perspective delineates the present status of the field from the efforts of othe...
Machine learning (ML) outperforms traditional approaches in many molecular design tasks. ML models usually predict molecular properties from a 2D chemical graph or a single 3D structure, but neither of these representations accounts for the ensemble ...
Fingerprint (FP) representations of chemical structure continue to be one of the most widely used types of molecular descriptors in chemoinformatics and computational medicinal chemistry. One often distinguishes between two- and three-dimensional (2D...
Journal of chemical theory and computation
35403426
Transition state searches are the basis for computationally characterizing reaction mechanisms, making them a pivotal tool in myriad chemical applications. Nevertheless, common search algorithms are sensitive to reaction conformations, and the confor...
Journal of the American Society for Mass Spectrometry
35378036
The interpretation of ion mobility coupled to mass spectrometry (IM-MS) data to predict unknown structures is challenging and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low o...
Machine learning models are widely applied to predict molecular properties or the biological activity of small molecules on a specific protein. Models can be integrated in a conformal prediction (CP) framework which adds a calibration step to estimat...