Papers › Tanimoto Random Features for Scalable Molecular Machine Learning

Tanimoto Random Features for Scalable Molecular Machine Learning

26 Jun 2023NeurIPS 2023 11arXiv:2306.14809archive 2025-07-28

Austin Tripp, Sergio Bacallado, Sukriti Singh, José Miguel Hernández-Lobato

The Tanimoto coefficient is commonly used to measure the similarity between molecules represented as discrete fingerprints, either as a distance metric or a positive definite kernel. While many kernel methods can be accelerated using random feature approximations, at present there is a lack of such approximations for the Tanimoto kernel. In this paper we propose two kinds of novel random features to allow this kernel to scale to large datasets, and in the process discover a novel extension of the kernel to real-valued vectors. We theoretically characterize these random features, and provide error bounds on the spectral norm of the Gram matrix. Experimentally, we show that these random features are effective at approximating the Tanimoto coefficient of real-world datasets and are useful for molecular property prediction and optimization tasks.

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