cs.LGNov 3, 2026

Molecular Prior Networks

Authors: Pawel Gomoluch, Anthony Bradley

Organizations: DaltonTx · Department of Chemistry, University of Liverpool

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Abstract

Predicting affinity between small molecules and their protein targets remains one of the central problems in drug discovery. Due to the substantial cost of wet-lab experiments, machine learning models of drug-target affinity are usually trained on relatively small datasets, ranging from tens to hundreds of molecules. Given the inherent difficulty of generalization between different targets and chemical series, existing datasets of protein-ligand affinities are typically not used for new projects. At the same time, tabular foundation models, including Prior-data Fitted Networks, have recently shown how transformers can be used to solve machine learning problems in a single forward pass of the network, after being trained over a large corpus of, typically, synthetic datasets. Inspired by that line of work, we propose a novel affinity modelling approach, in which the labels of the query molecules are predicted from the labelled support molecules in a single forward pass of a transformer model. The model is trained on thousands of existing affinity datasets, enabling it to capture the general patterns of molecular activity across different targets, chemical series and dataset sizes. For the smallest support sets, our model matches the state of the art on the regression variant of the FS-Mol benchmark, while being faster than existing methods, thanks to the lack of task-specific training. A comparison against a reduced variant of our architecture, which amounts to kernel regression in a meta-learned feature space, shows that the predictive performance of the transformer model is only partially explained by simple molecular similarity.

Figures & tables

 Support set size NsN_{s}
Model163264128256
Random forest0.1380.1980.2550.3100.354
GP-ST0.1630.2140.2620.3150.358
ADKF-IFT0.2980.3490.3910.4340.456
TabPFN0.1460.1900.2280.2760.322
TabICL (500)0.1500.1970.2470.3040.350
Table 1: Kendall’s τ\tau on the FS-Mol test tasks as a function of the support set size NsN_{s} . The Ns=256N_{s}=256 column is computed on the subset of tasks (43 out of 111) with enough samples to form a 256256 -molecule support set.
Figure 2
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Protein-Ligand Binding Affinity Prediction

Protein-ligand binding affinity prediction estimates how strongly a small molecule binds a protein to help prioritize compounds for drug discovery.

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