Atom- or bond-level chemical properties of interest in medicinal chemistry, such as drug metabolism and electrophilic reactivity, are important to understand and predict across arbitrary new molecules. Deep learning can be used to map molecular struc...
Journal of computer-aided molecular design
Oct 9, 2020
Atomistic simulations have become an invaluable tool for industrial applications ranging from the optimization of protein-ligand interactions for drug discovery to the design of new materials for energy applications. Here we review recent advances in...
We review progress in neural network (NN)-based methods for the construction of interatomic potentials from discrete samples (such as ab initio energies) for applications in classical and quantum dynamics including reaction dynamics and computational...
First-principles-based exploration of chemical space deepens our understanding of chemistry and might help with the design of new molecules, materials or experiments. Due to the computational cost of quantum chemistry methods and the immense number o...
Neural networks : the official journal of the International Neural Network Society
Jul 8, 2020
In this paper, a neural networks model for quantum computer is proposed. The core of this model is quantum neuron. Firstly, the inner product of the input qubits and the weight qubits is mapped to the phase of the control qubits in the neuron by the ...
This review provides an overview of descriptions of atoms applied to the understanding of phenomena like chemical reactivity and selectivity, pK values, Site of Metabolism prediction, or hydrogen bond strengths, but also the substitution of quantum m...
Journal of computer-aided molecular design
Mar 17, 2020
Scoring functions are routinely deployed in structure-based drug design to quantify the potential for protein-ligand (PL) complex formation. Here, we present a new scoring function Bappl+ that is designed to predict the binding affinities of non-meta...
Journal of chemical theory and computation
Nov 25, 2019
Sirtuin 5 is a class III histone deacetylase that, unlike its classification, mainly catalyzes desuccinylation and demanoylation reactions. It is an interesting drug target that we use here to test new ideas for calculating reaction pathways of large...
Inspecting protein and ligand electrostatic potential (ESP) surfaces in order to optimize electrostatic complementarity is a key activity in drug design. These ESP surfaces need to reflect the true electrostatic nature of the molecules, which typical...
There is growing interest in estimating quantum observables while circumventing expensive computational overhead for facile in silico materials screening. Machine learning (ML) methods are implemented to perform such calculations in shorter times. He...