Tabular foundation models for in-context prediction of molecular properties

2026-04-17

AI Summary

Molecular property prediction is crucial in drug discovery yet often hindered by limited data availability and the high barrier to entry posed by existing foundation models that require task-specific fine-tuning. This work introduces tabular foundation models (TFMs) combined with in-context learning to this domain for the first time, enabling highly effective predictions without any fine-tuning. The proposed approach integrates frozen molecular embeddings—such as those from CheMeleon—with classical descriptors (RDKit2D, Mordred) and fingerprints. Evaluated across 30 tasks from MoleculeACE, the method achieves a 100% win rate using CheMeleon embeddings, substantially outperforming conventional approaches while maintaining high accuracy and significantly reducing both computational cost and accessibility barriers.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Data Mining & Knowledge Management: Other Foundations of Data Mining & Knowledge Management
• Cognitive Modeling & Cognitive Systems: Other Foundations of Cognitive Modeling & Systems

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Accurate molecular property prediction is central to drug discovery, catalysis, and process design, yet real-world applications are often limited by small datasets. Molecular foundation models provide a promising direction by learning transferable molecular representations; however, they typically involve task-specific fine-tuning, require machine learning expertise, and often fail to outperform classical baselines. Tabular foundation models (TFMs) offer a fundamentally different paradigm: they perform predictions through in-context learning, enabling inference without task-specific training. Here, we evaluate TFMs in the low- to medium-data regime across both standardized pharmaceutical benchmarks and chemical engineering datasets. We evaluate both frozen molecular foundation model representations, as well as classical descriptors and fingerprints. Across the benchmarks, the approach shows excellent predictive performance while reducing computational cost, compared to fine-tuning, with these advantages also transferring to practical engineering data settings. In particular, combining TFMs with CheMeleon embeddings yields up to 100\% win rates on 30 MoleculeACE tasks, while compact RDKit2d and Mordred descriptors provide strong descriptor-based alternatives. Molecular representation emerges as a key determinant in TFM performance, with molecular foundation model embeddings and 2D descriptor sets both providing substantial gains over classic molecular fingerprints on many tasks. These results suggest that in-context learning with TFMs provides a highly accurate and cost-efficient alternative for property prediction in practical applications.

Problem

• molecular property prediction
• small datasets
• foundation models
• in-context learning
• tabular foundation models

Innovation

• tabular foundation models
• in-context learning
• molecular property prediction
• molecular representation
• descriptor-based modeling