… deep artificial neural networks with multiple layers. While the … and protein design is a protein
data set (eg, the Protein Data … peptides and protein structures in generative chemistry and …

… –structure relationships to empower inverse protein design? … a 1D deep convolutional neural
network, whereas our oracle … The labeled data set is split into training (70%), validation (15…

… of the neural network are assessed on a separate, independent test data-set. These …
methods based on language models (see Section 4.2) or in generative models that try to …

… designing novel protein sequences and structures using … More formally, we see our data set,
X, as a finite sample from … neural networks to a subtask in de novo protein design [125, 152, …

… decoders, equipped with recurrent neural networks (RNNs), … -tuning training procedure with
a small data set to prepare a … training strategy to better control the protein sequence design…

… design that utilizes generative recurrent neural networks (… have been used to predict protein
function from sequence7 … Ligands for each target were drawn from the ChEMBL data set. …

… — generating molecules that bind to specific protein binding sites. While we have witnessed
… convolutional neural networks and a crossdocked data set for structure-based drug design. …

… of properties exhibited on the training data set Additionally, different … deep generative models
and the underlying neural network … molecules as 3D structures, with the 3D structure of the …

… (25%) of the structures in the ZINC data set, we tried to increase the … source data set (structural
diversity) and target data set (protein–… Could graph neural networks learn better molecular …

… Our approach utilized a neural network trained with protein … atomistic protein structure. The
activation function used in the … To construct our data set for ICoN model training, we saved 1 …