Everything is connected: Graph neural networks
P Veličković - Current Opinion in Structural Biology, 2023 - Elsevier
In many ways, graphs are the main modality of data we receive from nature. This is due to
the fact that most of the patterns we see, both in natural and artificial systems, are elegantly …
the fact that most of the patterns we see, both in natural and artificial systems, are elegantly …
Machine-learning methods for ligand–protein molecular docking
K Crampon, A Giorkallos, M Deldossi, S Baud… - Drug discovery today, 2022 - Elsevier
Artificial intelligence (AI) is often presented as a new Industrial Revolution. Many domains
use AI, including molecular simulation for drug discovery. In this review, we provide an …
use AI, including molecular simulation for drug discovery. In this review, we provide an …
Equibind: Geometric deep learning for drug binding structure prediction
Predicting how a drug-like molecule binds to a specific protein target is a core problem in
drug discovery. An extremely fast computational binding method would enable key …
drug discovery. An extremely fast computational binding method would enable key …
Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction
Illuminating interactions between proteins and small drug molecules is a long-standing
challenge in the field of drug discovery. Despite the importance of understanding these …
challenge in the field of drug discovery. Despite the importance of understanding these …
On the frustration to predict binding affinities from protein–ligand structures with deep neural networks
Accurate prediction of binding affinities from protein–ligand atomic coordinates remains a
major challenge in early stages of drug discovery. Using modular message passing graph …
major challenge in early stages of drug discovery. Using modular message passing graph …
[HTML][HTML] Structure-based drug design with geometric deep learning
Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …
Learning substructure invariance for out-of-distribution molecular representations
Molecule representation learning (MRL) has been extensively studied and current methods
have shown promising power for various tasks, eg, molecular property prediction and target …
have shown promising power for various tasks, eg, molecular property prediction and target …
Interactiongraphnet: A novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictions
Accurate quantification of protein–ligand interactions remains a key challenge to structure-
based drug design. However, traditional machine learning (ML)-based methods based on …
based drug design. However, traditional machine learning (ML)-based methods based on …
Deep learning for drug repurposing: Methods, databases, and applications
Drug development is time‐consuming and expensive. Repurposing existing drugs for new
therapies is an attractive solution that accelerates drug development at reduced …
therapies is an attractive solution that accelerates drug development at reduced …
AlphaFold, artificial intelligence (AI), and allostery
R Nussinov, M Zhang, Y Liu, H Jang - The Journal of Physical …, 2022 - ACS Publications
AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of
biological sequence data and artificial intelligence (AI). AlphaFold has appended projects …
biological sequence data and artificial intelligence (AI). AlphaFold has appended projects …