MolLedger: An Additive Graph Neural Network with Chemically Grounded ADME Attributions
Optimizing absorption, distribution, metabolism, and excretion (ADME) is an important part of small molecule drug discovery. Many machine learning models have been built to predict ADME properties to facilitate this optimization process, but explaining model predictions is challenging. We propose a new graph neural network architecture with built-in atom attributions. Our model MolLedger learns a global context vector for each molecule and a per-atom head to output atom scores that sum to the predicted property. The atom scores are regularized to align with relevant chemical properties. We prove that MolLedger is a universal approximator and demonstrate empirically that the new architectu...
| Authors | Christina X. Ji |
|---|---|
| Primary category | cs.LG |
| Published | 31 August 2026 |
| Last updated at source | 24 September 2026 |
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