1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.LG2025★ 1 cited
Towards Faster and More Compact Foundation Models for Molecular Property Prediction
Yasir Ghunaim, Andrés Villa, Gergo Ignacz +3
Advancements in machine learning for molecular property prediction have improved accuracy but at the expense of higher computational cost and longer training times. Recently, the J…
cs.LG2025
On the Importance of Pretraining Data Alignment for Atomic Property Prediction
Yasir Ghunaim, Hasan Abed Al Kader Hammoud, Bernard Ghanem
This paper challenges the recent paradigm in atomic property prediction that links progress to growing dataset sizes and computational resources. We show that pretraining on a care…
q-bio.QM2024
Large-Scale Knowledge Integration for Enhanced Molecular Property Prediction
Yasir Ghunaim, Robert Hoehndorf
Pre-training machine learning models on molecular properties has proven effective for generating robust and generalizable representations, which is critical for advancements in dru…