Deep learning methods for molecular representation and property prediction
Highlights•The deep learning method could effectively represent the molecular structure and
predict molecular property through diversified models.•One, two, and three-dimensional …
predict molecular property through diversified models.•One, two, and three-dimensional …
Artificial intelligence in drug discovery: applications and techniques
Artificial intelligence (AI) has been transforming the practice of drug discovery in the past
decade. Various AI techniques have been used in many drug discovery applications, such …
decade. Various AI techniques have been used in many drug discovery applications, such …
Molecular contrastive learning of representations via graph neural networks
Molecular machine learning bears promise for efficient molecular property prediction and
drug discovery. However, labelled molecule data can be expensive and time consuming to …
drug discovery. However, labelled molecule data can be expensive and time consuming to …
Motif-based graph self-supervised learning for molecular property prediction
Predicting molecular properties with data-driven methods has drawn much attention in
recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable …
recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable …
Self-supervised graph transformer on large-scale molecular data
How to obtain informative representations of molecules is a crucial prerequisite in AI-driven
drug design and discovery. Recent researches abstract molecules as graphs and employ …
drug design and discovery. Recent researches abstract molecules as graphs and employ …
Fast and uncertainty-aware directional message passing for non-equilibrium molecules
Many important tasks in chemistry revolve around molecules during reactions. This requires
predictions far from the equilibrium, while most recent work in machine learning for …
predictions far from the equilibrium, while most recent work in machine learning for …
Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework
The clinical efficacy and safety of a drug is determined by its molecular properties and
targets in humans. However, proteome-wide evaluation of all compounds in humans, or …
targets in humans. However, proteome-wide evaluation of all compounds in humans, or …
[PDF][PDF] Spherical message passing for 3d molecular graphs
We consider representation learning of 3D molecular graphs in which each atom is
associated with a spatial position in 3D. This is an under-explored area of research, and a …
associated with a spatial position in 3D. This is an under-explored area of research, and a …
Torchmd-net: equivariant transformers for neural network based molecular potentials
The prediction of quantum mechanical properties is historically plagued by a trade-off
between accuracy and speed. Machine learning potentials have previously shown great …
between accuracy and speed. Machine learning potentials have previously shown great …
Large-scale chemical language representations capture molecular structure and properties
Abstract Models based on machine learning can enable accurate and fast molecular
property predictions, which is of interest in drug discovery and material design. Various …
property predictions, which is of interest in drug discovery and material design. Various …