Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions
New drug production, from target identification to marketing approval, takes over 12 years
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …
and can cost around $2.6 billion. Furthermore, the COVID-19 pandemic has unveiled the …
Deep learning in virtual screening: recent applications and developments
Drug discovery is a cost and time-intensive process that is often assisted by computational
methods, such as virtual screening, to speed up and guide the design of new compounds …
methods, such as virtual screening, to speed up and guide the design of new compounds …
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 …
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 …
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 …
Planet: a multi-objective graph neural network model for protein–ligand binding affinity prediction
X Zhang, H Gao, H Wang, Z Chen… - Journal of Chemical …, 2023 - ACS Publications
Predicting protein–ligand binding affinity is a central issue in drug design. Various deep
learning models have been published in recent years, where many of them rely on 3D …
learning models have been published in recent years, where many of them rely on 3D …
Hac-net: A hybrid attention-based convolutional neural network for highly accurate protein–ligand binding affinity prediction
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 …
Strategies of Artificial intelligence tools in the domain of nanomedicine
Nanomedicine is a field of medicine that uses nanotechnology to develop new diagnostic
tools and therapies for a wide range of medical conditions. It encompasses a variety of …
tools and therapies for a wide range of medical conditions. It encompasses a variety of …
De novo molecule design through the molecular generative model conditioned by 3D information of protein binding sites
De novo molecule design through the molecular generative model has gained increasing
attention in recent years. Here, a novel generative model was proposed by integrating the …
attention in recent years. Here, a novel generative model was proposed by integrating the …
Featurization strategies for protein–ligand interactions and their applications in scoring function development
The predictive performance of classical scoring functions (SFs) seems to have reached a
plateau. Currently, SFs relying on sophisticated machine learning techniques have shown …
plateau. Currently, SFs relying on sophisticated machine learning techniques have shown …