2 citations · 2 across the 4 of their papers we have counts for
5 papers
Extracting ontology-compliant knowledge from scientific text describing irradiated materials using large language models
Marco Luca Sbodio, Marcos Martínez Galindo, Vanessa Lopez +6
The quest for new materials increasingly relies on predictive models and comprehensive simulations that span scales from atomic to macroscopic levels. However, essential data neces…
Can Decision Trees Teach Large Language Models? Distilling Verbalized Knowledge for Molecular Property Prediction
Khiem Le, Sreejata Dey, Marcos Martínez Galindo +4
Molecular Property Prediction (MPP) is a fundamental problem in drug discovery that has recently attracted growing attention. Large Language Models (LLMs), known for their impressi…
WaveGAS: Waveform Relaxation for Scaling Graph Neural Networks
Jana Vatter, Mykhaylo Zayats, Marcos Martínez Galindo +4
With the ever-growing size of real-world graphs, numerous techniques to overcome resource limitations when training Graph Neural Networks (GNNs) have been developed. One such appro…
Description Boosting for Zero-Shot Entity and Relation Classification
Gabriele Picco, Leopold Fuchs, Marcos Martínez Galindo +3
Zero-shot entity and relation classification models leverage available external information of unseen classes -- e.g., textual descriptions -- to annotate input text data. Thanks t…
Otter-Knowledge: benchmarks of multimodal knowledge graph representation learning from different sources for drug discovery
Hoang Thanh Lam, Marco Luca Sbodio, Marcos Martínez Galindo +6
Recent research on predicting the binding affinity between drug molecules and proteins use representations learned, through unsupervised learning techniques, from large databases o…