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A practical guide to machine-learning scoring for structure-based virtual screening
Abstract Structure-based virtual screening (SBVS) via docking has been used to discover
active molecules for a range of therapeutic targets. Chemical and protein data sets that …
active molecules for a range of therapeutic targets. Chemical and protein data sets that …
[HTML][HTML] Protein–ligand docking in the machine-learning era
Molecular docking plays a significant role in early-stage drug discovery, from structure-
based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive …
based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive …
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 …
Multiscale topology-enabled structure-to-sequence transformer for protein–ligand interaction predictions
Despite the success of pretrained natural language processing (NLP) models in various
fields, their application in computational biology has been hindered by their reliance on …
fields, their application in computational biology has been hindered by their reliance on …
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 …
Integrated molecular modeling and machine learning for drug design
Modern therapeutic development often involves several stages that are interconnected, and
multiple iterations are usually required to bring a new drug to the market. Computational …
multiple iterations are usually required to bring a new drug to the market. Computational …
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 …
Modern machine‐learning for binding affinity estimation of protein–ligand complexes: Progress, opportunities, and challenges
Abstract Structure‐based drug design is a widely applied approach in the discovery of new
lead compounds for known therapeutic targets. In most structure‐based drug design …
lead compounds for known therapeutic targets. In most structure‐based drug design …
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 …
Different applications of machine learning approaches in materials science and engineering: Comprehensive review
Over the last decades, considerable advancements in artificial intelligence (AI) approaches
have eventuated in their extensive applications in all scientific scopes such as materials …
have eventuated in their extensive applications in all scientific scopes such as materials …