Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, hel** scientists to generate hypotheses, design experiments …

Drug discovery is adapting to novel technologies such as data science, informatics, and
artificial intelligence (AI) to accelerate effective treatment development while reducing costs …

There is increasing adoption of artificial intelligence in drug discovery. However, existing
studies use machine learning to mainly utilize the chemical structures of molecules but …

Deep learning models can accurately predict molecular properties and help making the
search for potential drug candidates faster and more efficient. Many existing methods are …

Abstract Graph Neural Networks (GNNs) that are based on the message passing (MP)
paradigm generally exchange information between 1-hop neighbors to build node …

Molecular representation learning (MRL) has gained tremendous attention due to its critical
role in learning from limited supervised data for applications like drug design. In most MRL …

Highlights•The deep learning method could effectively represent the molecular structure and
predict molecular property through diversified models.•One, two, and three-dimensional …

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 …

Large Language Models (LLMs) have emerged as a transformative power in enhancing
natural language comprehension, representing a significant stride toward artificial general …

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 …