Scoring functions for protein-ligand binding affinity prediction using structure-based deep learning: a review
The rapid and accurate in silico prediction of protein-ligand binding free energies or binding
affinities has the potential to transform drug discovery. In recent years, there has been a …
affinities has the potential to transform drug discovery. In recent years, there has been a …
Delta machine learning to improve scoring-ranking-screening performances of protein–ligand scoring functions
Protein–ligand scoring functions are widely used in structure-based drug design for fast
evaluation of protein–ligand interactions, and it is of strong interest to develop scoring …
evaluation of protein–ligand interactions, and it is of strong interest to develop scoring …
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 …
PIGNet: a physics-informed deep learning model toward generalized drug–target interaction predictions
Recently, deep neural network (DNN)-based drug–target interaction (DTI) models were
highlighted for their high accuracy with affordable computational costs. Yet, the models' …
highlighted for their high accuracy with affordable computational costs. Yet, the models' …
A generalized protein–ligand scoring framework with balanced scoring, docking, ranking and screening powers
Applying machine learning algorithms to protein–ligand scoring functions has aroused
widespread attention in recent years due to the high predictive accuracy and affordable …
widespread attention in recent years due to the high predictive accuracy and affordable …
Hac-net: A hybrid attention-based convolutional neural network for highly accurate protein–ligand binding affinity prediction
GW Kyro, RI Brent, VS Batista - Journal of Chemical Information …, 2023 - ACS Publications
Applying deep learning concepts from image detection and graph theory has greatly
advanced protein–ligand binding affinity prediction, a challenge with enormous ramifications …
advanced protein–ligand binding affinity prediction, a challenge with enormous ramifications …
Open-source machine learning in computational chemistry
A Hagg, KN Kirschner - Journal of Chemical Information and …, 2023 - ACS Publications
The field of computational chemistry has seen a significant increase in the integration of
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …
machine learning concepts and algorithms. In this Perspective, we surveyed 179 open …
[HTML][HTML] DTITR: End-to-end drug–target binding affinity prediction with transformers
The accurate identification of Drug–Target Interactions (DTIs) remains a critical turning point
in drug discovery and understanding of the binding process. Despite recent advances in …
in drug discovery and understanding of the binding process. Despite recent advances in …
PIGNet2: a versatile deep learning-based protein–ligand interaction prediction model for binding affinity scoring and virtual screening
Prediction of protein–ligand interactions (PLI) plays a crucial role in drug discovery as it
guides the identification and optimization of molecules that effectively bind to target proteins …
guides the identification and optimization of molecules that effectively bind to target proteins …
Deepbindgcn: Integrating molecular vector representation with graph convolutional neural networks for protein–ligand interaction prediction
H Zhang, KM Saravanan, JZH Zhang - Molecules, 2023 - mdpi.com
The core of large-scale drug virtual screening is to select the binders accurately and
efficiently with high affinity from large libraries of small molecules in which non-binders are …
efficiently with high affinity from large libraries of small molecules in which non-binders are …