Opportunities and challenges in explainable artificial intelligence (xai): A survey
Nowadays, deep neural networks are widely used in mission critical systems such as
healthcare, self-driving vehicles, and military which have direct impact on human lives …
healthcare, self-driving vehicles, and military which have direct impact on human lives …
[HTML][HTML] New avenues in artificial-intelligence-assisted drug discovery
C Cerchia, A Lavecchia - Drug Discovery Today, 2023 - Elsevier
Over the past decade, the amount of biomedical data available has grown at unprecedented
rates. Increased automation technology and larger data volumes have encouraged the use …
rates. Increased automation technology and larger data volumes have encouraged the use …
Benchmarking AlphaFold‐enabled molecular docking predictions for antibiotic discovery
Efficient identification of drug mechanisms of action remains a challenge. Computational
docking approaches have been widely used to predict drug binding targets; yet, such …
docking approaches have been widely used to predict drug binding targets; yet, such …
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 …
Exposing the limitations of molecular machine learning with activity cliffs
D Van Tilborg, A Alenicheva… - Journal of chemical …, 2022 - ACS Publications
Machine learning has become a crucial tool in drug discovery and chemistry at large, eg, to
predict molecular properties, such as bioactivity, with high accuracy. However, activity …
predict molecular properties, such as bioactivity, with high accuracy. However, activity …
Evaluation guidelines for machine learning tools in the chemical sciences
Abstract Machine learning (ML) promises to tackle the grand challenges in chemistry and
speed up the generation, improvement and/or ordering of research hypotheses. Despite the …
speed up the generation, improvement and/or ordering of research hypotheses. Despite the …
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 …
TransformerCPI: improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments
Motivation Identifying compound–protein interaction (CPI) is a crucial task in drug discovery
and chemogenomics studies, and proteins without three-dimensional structure account for a …
and chemogenomics studies, and proteins without three-dimensional structure account for a …
Graph neural networks for automated de novo drug design
Highlights•GNN has attracted wide attention from the field of designing drug molecules.•The
applications of GNN in molecule scoring, molecule generation and optimization, and …
applications of GNN in molecule scoring, molecule generation and optimization, and …