3 citations · 4 across the 3 of their papers we have counts for
3 papers
Interpretable Deep Learning for Polar Mechanistic Reaction Prediction
Ryan J. Miller, Alexander E. Dashuta, Brayden Rudisill +2
Accurately predicting chemical reactions is essential for driving innovation in synthetic chemistry, with broad applications in medicine, manufacturing, and agriculture. At the sam…
AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning
Mohammadamin Tavakoli, Yin Ting T. Chiu, Alexander Shmakov +3
Deep learning-based reaction predictors have undergone significant architectural evolution. However, their reliance on reactions from the US Patent Office results in a lack of inte…
Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity
Mohammadamin Tavakoli, Aaron Mood, David Van Vranken +1
There is a lack of scalable quantitative measures of reactivity for functional groups in organic chemistry. Measuring reactivity experimentally is costly and time-consuming and doe…