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MolLedger: An Additive Graph Neural Network with Chemically Grounded ADME Attributions

ArXiv|Monday, August 31, 2026|about 17 hours ago|Christina X. Ji

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Why It Matters

Optimizing absorption, distribution, metabolism, and excretion (ADME) is an important part of small molecule drug discovery.

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Key Claims

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 meaningful per-atom attributions.
Our model MolLedger outputs predictions that are the sum of per-atom scores.

Original description

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 meaningful per-atom attributions. Our model MolLedger outputs predictions that are the sum of per-atom scores. MolLedger's additive framework obtains exact interpretability at no cost to performance because the global context vector gives the addit...

arxiv cs.LG publisher:arxiv tier:research

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ID: http://arxiv.org/abs/2608.30636v1 · Indexed about 2 hours ago