Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces

• URL: http://arxiv.org/abs/2111.13964v1
• Date: Sat, 27 Nov 2021 18:54:38 GMT
• Title: Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces
• Authors: Fabio Broccatelli, Richard Trager, Michael Reutlinger, George Karypis, Mufei Li
• Abstract summary: We consider four graph neural network (GNN) variants -- Graph Convolutional Network (GCN), Graph Attention Network (GAT), Message Passing Neural Network (MPNN) and Attentive Fingerprint (AttentiveFP) All GNN models significantly outperform lower-bar benchmark traditional models solely based on fingerprints. Only GATs seem to offer a small but consistent improvement over higher-bar benchmark traditional models.
• Score: 6.118940071203314
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this work, we benchmark a variety of single- and multi-task graph neural network (GNN) models against lower-bar and higher-bar traditional machine learning approaches employing human engineered molecular features. We consider four GNN variants -- Graph Convolutional Network (GCN), Graph Attention Network (GAT), Message Passing Neural Network (MPNN), and Attentive Fingerprint (AttentiveFP). So far deep learning models have been primarily benchmarked using lower-bar traditional models solely based on fingerprints, while more realistic benchmarks employing fingerprints, whole-molecule descriptors and predictions from other related endpoints (e.g., LogD7.4) appear to be scarce for industrial ADME datasets. In addition to time-split test sets based on Genentech data, this study benefits from the availability of measurements from an external chemical space (Roche data). We identify GAT as a promising approach to implementing deep learning models. While all GNN models significantly outperform lower-bar benchmark traditional models solely based on fingerprints, only GATs seem to offer a small but consistent improvement over higher-bar benchmark traditional models. Finally, the accuracy of in vitro assays from different laboratories predicting the same experimental endpoints appears to be comparable with the accuracy of GAT single-task models, suggesting that most of the observed error from the models is a function of the experimental error propagation.

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