Increasing trustworthiness of machine learning-based drug sensitivity prediction with a multivariate random forest approach

MORGOTH is a comprehensive approach that unites simultaneous regression and classification, interpretability, reliability, and robustness in a single framework. We demonstrate its capabilities to predict anti-cancer drug responses.

Authors

Lea Eckhart (ORCID: https://orcid.org/0000-0002-1486-3267)
Kerstin Lenhof (ORCID: https://orcid.org/0000-0003-1311-0367)
Lisa-Marie Rolli (ORCID: https://orcid.org/0009-0007-7860-8656)
Hans-Peter Lenhof
Lutz Herrmann
Andrea Volkamer

Institutions

ETH Zurich (CH)
Artificial Intelligence in Medicine (Canada) (CA)
University of Göttingen (DE)
Saarland University (DE)

Publication Details

Journal: Digital Discovery
Published: 2026-01-01
DOI: https://doi.org/10.1039/d5dd00284b
Primary Topic: Computational Drug Discovery Methods
Type: article
Field-Weighted Citation Impact: 0.00

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article

Increasing trustworthiness of machine learning-based drug sensitivity prediction with a multivariate random forest approach

Lea Eckhart, Kerstin Lenhof, Lisa-Marie Rolli, Hans-Peter Lenhof et al.

Jan 1, 2026

Digital Discovery

Computational Drug Discovery Methods

article

Increasing trustworthiness of machine learning-based drug sensitivity prediction with a multivariate random forest approach

Lea Eckhart, Kerstin Lenhof, Lisa-Marie Rolli, Hans-Peter Lenhof, Lutz Herrmann, Andrea Volkamer

January 1, 2026article en

Abstract

MORGOTH is a comprehensive approach that unites simultaneous regression and classification, interpretability, reliability, and robustness in a single framework. We demonstrate its capabilities to predict anti-cancer drug responses.

Digital Discovery

ETH Zurich (CH), Artificial Intelligence in Medicine (Canada) (CA), University of Göttingen (DE), Saarland University (DE)

Openalex Percentile: Top 8%

Computational Drug Discovery Methods

DOI Open Access PDF OpenAlex

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