Article-Journal

Reviews existing SARS-CoV-2 neutralizing antibodies and evaluates their therapeutic potential using topological data analysis and deep learning models.

Introduces persistent spectral theory as a unified multiscale paradigm to reveal topological persistence and extract geometric shapes from high-dimensional datasets for data analysis.

This work develops a generative network complex (GNC) that optimizes multiple chemical properties in a latent space to automatically generate novel, drug-like molecules.

This paper advocates for drug repositioning as a rapid and cost-effective strategy to identify potential anti-SARS-CoV-2 therapies from existing drug databases.

Presents MathDL models combining advanced mathematics and deep learning that achieved top performance in pose prediction and affinity ranking in D3R Grand Challenge 4.

This paper introduces a boosting tree-assisted multitask deep learning architecture that integrates gradient boosting and multitask learning to achieve optimal predictions for small scientific datasets.

This review highlights recent advances in low-dimensional mathematical representations of biomolecules—using algebraic topology, differential geometry, and graph theory—to enhance machine learning performance in computational biology.

This study evaluates 2D fingerprints in drug discovery, demonstrating that when paired with advanced machine learning, they perform comparably to 3D methods for ligand-based tasks but lag in complex-based binding affinity predictions.

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.

The AGL-Score models proposed in this study utilize algebraic graph learning to encode molecular information, significantly outperforming existing scoring functions in protein-ligand binding tasks.