mlsanityData to 7 Oct 2026

Tanimoto Random Features for Scalable Molecular Machine Learning

arXiv 2306.14809 ↗First posted 26 Jun 2023cs.LGcited by 15

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Abstract (arXiv)

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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 17 of its 75 references that are in the map. First posted on arXiv 26 Jun 2023.

Builds on

17 of 75 references.

References · 17

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
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  12. 2021DOCKSTRING: easy molecular docking yields better benchmarks for ligand designcited by 33
5 more
  1. 2016A note on the triangle inequality for the Jaccard distancecited by 32
  2. 2021Scaling Neural Tangent Kernels via Sketching and Random Featurescited by 32
  3. 2021Further Generalizations of the Jaccard Indexcited by 26
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  5. 2022Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernelcited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 6; 2026: 4.

15 papers.

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  1. 2023Stochastic Gradient Descent for Gaussian Processes Done Rightcited by 16
  2. 2025Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spacescited by 13
  3. 2025Tensor Sketch: Fast and Scalable Polynomial Kernel Approximationcited by 4
  4. 2024Sort & Slice: A Simple and Superior Alternative to Hash-Based Folding for Extended-Connectivity Fingerprintscited by 3
  5. 2024Explainable Molecular Property Prediction: Aligning Chemical Concepts with Predictions via Language Modelscited by 3
  6. 2024Variance-Reducing Couplings for Random Featurescited by 2
  7. 2024Deep Sketched Output Kernel Regression for Structured Predictioncited by 2
  8. 2025CRPS-Based Targeted Sequential Design with Application in Chemical Spacecited by 2
  9. 2026Time-Aware Latent Space Bayesian Optimizationcited by 1
  10. 2025On the Usage of Gaussian Process for Efficient Data Valuationcited by 0
  11. 2025Is Sequence Information All You Need for Bayesian Optimization of Antibodies?cited by 0
  12. 2025A decoupled alignment kernel for peptide membrane permeability predictionscited by 0
3 more
  1. 2026Active Learning for Gaussian Process Regression Under Self-Induced Boltzmann Weightscited by 0
  2. 2026On the post-hoc Evaluation of PDE Discovery: A Multifaceted Challenge of Scientific Advancementcited by 0
  3. 2026PGFS++: Molecular Property Improvement under Synthesis and Diversity Constraintscited by 0