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 …
Autonomous discovery in the chemical sciences part I: Progress
This two‐part Review examines how automation has contributed to different aspects of
discovery in the chemical sciences. In this first part, we describe a classification for …
discovery in the chemical sciences. In this first part, we describe a classification for …
RCSB Protein Data Bank (RCSB. org): delivery of experimentally-determined PDB structures alongside one million computed structure models of proteins from …
Abstract The Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB
PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center …
PDB), founding member of the Worldwide Protein Data Bank (wwPDB), is the US data center …
Uni-mol: A universal 3d molecular representation learning framework
Molecular representation learning (MRL) has gained tremendous attention due to its critical
role in learning from limited supervised data for applications like drug design. In most MRL …
role in learning from limited supervised data for applications like drug design. In most MRL …
Equivariant 3D-conditional diffusion model for molecular linker design
Fragment-based drug discovery has been an effective paradigm in early-stage drug
development. An open challenge in this area is designing linkers between disconnected …
development. An open challenge in this area is designing linkers between disconnected …
GNINA 1.0: molecular docking with deep learning
Molecular docking computationally predicts the conformation of a small molecule when
binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline …
binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline …
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 …
Boosting protein–ligand binding pose prediction and virtual screening based on residue–atom distance likelihood potential and graph transformer
The past few years have witnessed enormous progress toward applying machine learning
approaches to the development of protein–ligand scoring functions. However, the robust …
approaches to the development of protein–ligand scoring functions. However, the robust …
Structure-aware interactive graph neural networks for the prediction of protein-ligand binding affinity
Drug discovery often relies on the successful prediction of protein-ligand binding affinity.
Recent advances have shown great promise in applying graph neural networks (GNNs) for …
Recent advances have shown great promise in applying graph neural networks (GNNs) for …
New machine learning and physics-based scoring functions for drug discovery
Scoring functions are essential for modern in silico drug discovery. However, the accurate
prediction of binding affinity by scoring functions remains a challenging task. The …
prediction of binding affinity by scoring functions remains a challenging task. The …