Deep-Learning

Novel Molecular Representations Using Neumann-Cayley Orthogonal Gated Recurrent Unit
This study proposes a new molecular fingerprinting method using Neumann-Cayley Gated Recurrent Units (NC-GRU) within an autoencoder to enhance molecular property prediction.
Cite Code DOI
AweGNN: Auto-parametrized weighted element-specific graph neural networks for molecules
Introduces AweGNN, a graph neural network with auto-parametrized weighted element-specific features for molecular property prediction.
Cite Code DOI
Review of COVID-19 Antibody Therapies
Reviews existing SARS-CoV-2 neutralizing antibodies and evaluates their therapeutic potential using topological data analysis and deep learning models.
Cite DOI
Generative Network Complex for the Automated Generation of Drug-like Molecules
This work develops a generative network complex (GNC) that optimizes multiple chemical properties in a latent space to automatically generate novel, drug-like molecules.
Cite DOI
Unveiling the molecular mechanism of SARS-CoV-2 main protease inhibition from 137 crystal structures using algebraic topology and deep learning
Integrates algebraic topology and deep learning (MathDL) to predict binding affinities and rank 137 SARS-CoV-2 main protease (Mpro) inhibitor structures, revealing key binding sites and interactions.
Cite DOI