2 citations · 2 across the 3 of their papers we have counts for
3 papers
Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modeling
Alessio Fallani, Ramil Nugmanov, Jose Arjona-Medina +3
We evaluate the impact of pretraining Graph Transformer architectures on atom-level quantum-mechanical features for the modeling of absorption, distribution, metabolism, excretion,…
Analysis of Atom-level pretraining with Quantum Mechanics (QM) data for Graph Neural Networks Molecular property models
Jose Arjona-Medina, Ramil Nugmanov
Despite the rapid and significant advancements in deep learning for Quantitative Structure-Activity Relationship (QSAR) models, the challenge of learning robust molecular represent…
ReacLLaMA: Merging chemical and textual information in chemical reactivity AI models
Aline Hartgers, Ramil Nugmanov, Kostiantyn Chernichenko +1
Chemical reactivity models are developed to predict chemical reaction outcomes in the form of classification (success/failure) or regression (product yield) tasks. The vast majorit…