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The de novo design of drug molecules is recognized as a time-consuming and costly process, and computational approaches have been applied in each stage of the drug discovery pipeline. Variational ...
This repository contains the implementation of MMO-VAE, a Variational AutoEncoder (VAE) with mask-guided multi-objective optimization for de novo drug design. Implements KLD Sigmoid Annealing and ...
DrugHIVE: Structure-based drug design with a deep hierarchical generative model This repository is the official implementation of DrugHIVE, a deep hierarchical variational autoencoder developed for ...
Using a variational autoencoder, we are able to generate a latent space plot that can be surveyed for peptides with known properties and interpolated across a predictive vector between two defined ...
Deep generative models for molecular generation have accelerated the development of de novo drug design by introducing how to generate novel molecular structures expressed in simplified ...
To summarize, the above results suggest that a variational autoencoder with 4 hidden layers in both of the encoder and decoder modules exhibited the best performance in terms of learning a meaningful ...
Schematic of variational autoencoder (VAE) antimicrobial peptide (AMP) generation and design process. The VAE AMP generation and design process occurs in two stages: (1) training the VAE for the ...
In this work, we propose a machine learning approach to generate novel molecules starting from a seed compound, its three-dimensional (3D) shape, and its pharmacophoric features. The pipeline draws ...
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