Recently, transformer architectures for graphs emerged as an alternative to established
techniques for machine learning with graphs, such as (message-passing) graph neural …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

Quantum chemical (QC) property prediction is crucial for computational materials and drug
design, but relies on expensive electronic structure calculations like density functional theory …

The precise prediction of molecular properties is essential for advancements in drug
development, particularly in virtual screening and compound optimization. The recent …

Creating a successful small molecule drug is a challenging multiparameter optimization
problem in an effectively infinite space of possible molecules. Generative models have …

The emergence of foundation models in Computer Vision and Natural Language Processing
have resulted in immense progress on downstream tasks. This progress was enabled by …

Molecular property prediction is an important problem in drug discovery and materials
science. As geometric structures have been demonstrated necessary for molecular property …

The powerful data processing and pattern recognition capabilities of machine learning (ML)
technology have provided technical support for the innovation in computational chemistry …

Molecular modeling, a central topic in quantum mechanics, aims to accurately calculate the
properties and simulate the behaviors of molecular systems. The molecular model is …

Recent developments in deep learning have made remarkable progress in speeding up the
prediction of quantum chemical (QC) properties by removing the need for expensive …