Runar Helin

• Paul, Clarke; Cmelo, Ivan; Helin, Runar; Shende, Mayur Kishor; Granmo, Ole-Christoffer & Fayne, Darren (2026). The Tsetlin Machine: A “Third Way” in QSAR Modeling. Journal of Chemical Information and Modeling. ISSN 1549-9596. 66(11), p. 6250–6270. doi: 10.1021/acs.jcim.5c03109. Full text in Research Archive Show summary

  Advances in Quantitative Structure Activity Relationship (QSAR) are led by two core paradigms, (1) descriptor engineering, where complex fixed-length vectors of compounds are generated and conventional ML methods are applied to those representations and (2) graphical chemical inputs (e.g., Simplified Molecular Input Line Entry System (SMILES), 2D-graph) being provided to deep learning neural network (NN) models, which construct their own internal representations of molecules and learn iteratively over them. Here we present the Tsetlin Machine (TM)─which combines the accuracy and easy-use of existing rule-based QSAR ML methods (e.g., RF and XGBoost), the iterative learning aspect of NN algorithms and its intrinsic interpretability. The TM uses teams of finite-state automata which capture frequent patterns as propositional logic (clauses) via reinforcement learning. The benchmarking pipeline presented here demonstrates that TM-QSAR coupled with ECFP4 descriptors frequently performs better than existing rule-based QSAR methods for ROC-AUC, PRC-AUC and PPV, with a high capacity for interscaffold generalization. However, due to the binary nature of TM-QSAR, performance it is currently limited when descretised continuous descriptors are used. TM-QSAR demonstrated particularly impressive classification scores for MOR (ROC-AUC = 0.87, PRC-AUC = 0.77) and CYPA4 (ROC-AUC = 0.92, PRC-AUC = 0.63), when compared to RF and XGBoost. Using TM in combination with substructural fingerprinting descriptors allows for an interpretability suite which can be extracted directly from clauses. Here we detail molecule property maps (TM-MPM) to view atom-wise TM-QSAR bioactivity contributions for single molecules and closed-form WAC scores (Weights × Activations × Clauses) for descriptor-wise contributions to regions of predicted chemical space. These methods show strong alignment of TM-QSAR interpretations to known ligand–protein interactions of the MOR target and gives nonlinear, conditional interpretations for greater predicted bioactivity. Given this combination of accuracy, computational efficiency and interpretability, we provide a basis for TM-QSAR to be explored as a standard methodology in virtual screening toolkits.

• Helin, Runar; Indahl, Ulf Geir; Tomic, Oliver & Liland, Kristian Hovde (2023). Non-linear shrinking of linear model errors. Analytica Chimica Acta. ISSN 0003-2670. 1258. doi: 10.1016/j.aca.2023.341147. Full text in Research Archive
• Jenul, Anna Selina; Schrunner, Stefan; Huynh, Bao Ngoc; Helin, Runar; Futsæther, Cecilia Marie & Liland, Kristian Hovde [Show all 7 contributors for this article] (2022). Ranking Feature-Block Importance in Artificial Multiblock Neural Networks. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 13532. doi: 10.1007/978-3-031-15937-4_14. Full text in Research Archive
• Helin, Runar; Indahl, Ulf Geir; Tomic, Oliver & Liland, Kristian Hovde (2021). On the possible benefits of deep learning for spectral preprocessing. Journal of Chemometrics. ISSN 0886-9383. doi: 10.1002/cem.3374. Full text in Research Archive

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• Helin, Runar; Indahl, Ulf Geir; Tomic, Oliver & Liland, Kristian Hovde (2021). On the possible benefits of deep learning for spectral pre-processing. Full text in Research Archive

View all works in NVA