Activity and property prediction models are the central workhorses in drug discovery and
materials sciences, but currently, they have to be trained or fine-tuned for new tasks. Without …

Graph transformers have been competitive on graph classification tasks, but they fail to
outperform Graph Neural Networks (GNNs) on node classification, which is a common task …

Predicting the biological properties of molecules is crucial in computer-aided drug
development, yet it's often impeded by data scarcity and imbalance in many practical …

Procuring expressive molecular representations underpins AI-driven molecule design and
scientific discovery. The research mainly focuses on atom-level homogeneous molecular …

The widespread adoption of Transformer architectures in various data modalities has
opened new avenues for the applications in molecular modeling. Nevertheless, it remains …

By accelerating time-consuming processes with high efficiency, computing has become an
essential part of many modern chemical pipelines. Machine learning is a class of computing …

The success of language models, especially transformer-based architectures, has trickled
into other domains giving rise to" scientific language models" that operate on small …

In many scientific fields, large language models (LLMs) have revolutionized the way with
which text and other modalities of data (eg, molecules and proteins) are dealt, achieving …

Transformers have become methods of choice in many applications thanks to their ability to
represent complex interactions between elements. However, extending the Transformer …

To solve the problems of polysemy and feature extraction in the text sentiment analysis
process, a BERT-CNN-BiLSTM-Att hybrid model has been proposed for text sentiment …