A comprehensive survey on deep graph representation learning
Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …
structured data into low-dimensional dense vectors, which is a fundamental task that has …
Dataset for quantum-mechanical exploration of conformers and solvent effects in large drug-like molecules
We here introduce the Aquamarine (AQM) dataset, an extensive quantum-mechanical (QM)
dataset that contains the structural and electronic information of 59,783 low-and high-energy …
dataset that contains the structural and electronic information of 59,783 low-and high-energy …
Inverse map** of quantum properties to structures for chemical space of small organic molecules
Computer-driven molecular design combines the principles of chemistry, physics, and
artificial intelligence to identify chemical compounds with tailored properties. While quantum …
artificial intelligence to identify chemical compounds with tailored properties. While quantum …
Crash testing machine learning force fields for molecules, materials, and interfaces: Molecular dynamics in the TEA challenge 2023
We present the second part of the rigorous evaluation of modern machine learning force
fields (MLFFs) within the TEA Challenge 2023. This study provides an in-depth analysis of …
fields (MLFFs) within the TEA Challenge 2023. This study provides an in-depth analysis of …
GraphADT: empowering interpretable predictions of acute dermal toxicity with multi-view graph pooling and structure remap**
Motivation Accurate prediction of acute dermal toxicity (ADT) is essential for the safe and
effective development of contact drugs. Currently, graph neural networks, a form of deep …
effective development of contact drugs. Currently, graph neural networks, a form of deep …
Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction
The attrition rate of drugs in clinical trials is generally quite high, with estimates suggesting
that approximately 90% of drugs fail to make it through the process. The identification of …
that approximately 90% of drugs fail to make it through the process. The identification of …
SolPredictor: predicting solubility with residual gated graph neural network
Computational methods play a pivotal role in the pursuit of efficient drug discovery, enabling
the rapid assessment of compound properties before costly and time-consuming laboratory …
the rapid assessment of compound properties before costly and time-consuming laboratory …
Carbohydrate NMR chemical shift prediction by GeqShift employing E (3) equivariant graph neural networks
Carbohydrates, vital components of biological systems, are well-known for their structural
diversity. Nuclear Magnetic Resonance (NMR) spectroscopy plays a crucial role in …
diversity. Nuclear Magnetic Resonance (NMR) spectroscopy plays a crucial role in …
Equivariant neural networks utilizing molecular clusters for accurate molecular crystal lattice energy predictions
Equivariant neural networks have emerged as prominent models in advancing the
construction of interatomic potentials due to their remarkable data efficiency and …
construction of interatomic potentials due to their remarkable data efficiency and …
Introduction to the Special Issue: AI Meets Toxicology
Artificial intelligence (AI) has made significant contributions to various scientific disciplines
including Toxicology. 1, 2 Recently, the emergence of large language models with …
including Toxicology. 1, 2 Recently, the emergence of large language models with …