arXiv:2605.30195v1 Announce Type: cross Abstract: Message-passing neural networks (MPNNs) are widely used for molecular property prediction, but their deployment as monolithic architectures makes it difficult to identify how specific message-passing operators affect performance. We present an operator-level factorial benchmark that decomposes 2D molecular MPNNs into the three families of message-seed initialization, node-edge fusion, and node update operators. The resulting 84 configurations are benchmarked on ten MoleculeNet datasets under a shared experimental setup and statistical analysis
The paper provides a detailed benchmark for understanding and improving molecular MPNNs at a time of rapid advancements in AI for scientific discovery.
Improving the performance and interpretability of MPNNs is crucial for accelerating progress in material science and drug discovery, impacting various industries.
This research provides a more granular understanding of how different components of MPNNs contribute to performance, enabling more targeted development and optimization of AI models for molecular property prediction.
- · AI researchers in chemistry
- · Pharmaceutical companies
- · Material science companies
- · Drug discovery platforms
More efficient and accurate molecular simulations will lead to faster development cycles for new materials and drugs.
Reduced R&D costs and accelerated time-to-market for products in biotech and advanced materials sectors.
Enhanced capabilities could enable the discovery of completely novel compounds with unprecedented properties, driving new industrial applications.
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Read at arXiv cs.LG