Automated computational analysis of the vast chemical space is critical for numerous fields
of research such as drug discovery and material science. Representation learning …

Generative machine learning models have attracted intense interest for their ability to
sample novel molecules with desired chemical or biological properties. Among these …

With the emergence of Transformer architectures and their powerful understanding of textual
data, a new horizon has opened up to predict the molecular properties based on text …

Web ontologies are important tools in modern scientific research because they provide a
standardized way to represent and manage web-scale amounts of complex data. In …

The success of language models, especially transformer-based architectures, has trickled
into other scientific domains, giving rise to the concept of “scientific language models” that …

Stakeholders of machine learning models desire explainable artificial intelligence (XAI) to
produce human-understandable and consistent interpretations. In computational toxicity …

Neural network models have become a popular machine-learning technique for the toxicity
prediction of chemicals. However, due to their complex structure, it is difficult to understand …

Artificial intelligence (AI) contributes new methods for designing compounds in drug
discovery, ranging from de novo design models suggesting new molecular structures or …

PROTACs are a promising therapeutic modality that harnesses the cell's built-in degradation
machinery to degrade specific proteins. Despite their potential, developing new PROTACs is …

Structural alerts (SAs) are essential to identify chemicals for toxicity evaluation and health
risk assessment. We constructed a novel SMILES split-based deep learning model (SSDL) …