Journal of chemical theory and computation
Jul 15, 2022
Existing computational methods for estimating p values in proteins rely on theoretical approximations and lengthy computations. In this work, we use a data set of 6 million theoretically determined p shifts to train deep learning models, which are sh...
Journal of chemical information and modeling
Jul 5, 2022
The principle of least action is the cornerstone of classical mechanics, theory of relativity, quantum mechanics, and thermodynamics. Here, we describe how a neural network (NN) learns to find the trajectory for a Lennard-Jones (LJ) system that maint...
European journal of medicinal chemistry
Jul 1, 2022
Artificial intelligence (AI) has been recognized as a powerful technique that can accelerate drug discovery during the hit compound identification step. However, most simple deep learning models have been used for naive pre-filtering as the predictio...
NIMA-related kinase7 (NEK7) plays a multifunctional role in cell division and NLRP3 inflammasone activation. A typical expression or any mutation in the genetic makeup of NEK7 leads to the development of cancer malignancies and fatal inflammatory dis...
INTRODUCTION The eukaryotic nucleus pro-tects the genome and is enclosed by the two membranes of the nuclear envelope. Nuclear pore complexes (NPCs) perforate the nuclear envelope to facilitate nucleocytoplasmic transport. With a molecular weight of ...
The journal of physical chemistry letters
May 6, 2022
molecular dynamics (AIMD) is an established method for revealing the reactive dynamics of complex systems. However, the high computational cost of AIMD restricts the explorable length and time scales. Here, we develop a fundamentally different appro...
This work presents Neural Equivariant Interatomic Potentials (NequIP), an E(3)-equivariant neural network approach for learning interatomic potentials from ab-initio calculations for molecular dynamics simulations. While most contemporary symmetry-aw...
Journal of chemical information and modeling
Apr 14, 2022
Graph neural network (GNN)-based deep learning (DL) models have been widely implemented to predict the experimental aqueous solvation free energy, while its prediction accuracy has reached a plateau partly due to the scarcity of available experimenta...
Journal of chemical theory and computation
Apr 6, 2022
An effective implementation of enhanced sampling algorithms for molecular dynamics simulations requires knowledge of the approximate reaction coordinate describing the relevant mechanisms in the system. In this work, we focus on the recently develop...
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