2nd Place Winner – Tox24 Challenge 

Challenge: Prediction of compounds activity against Transthyretin (TTR)

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Organisers: Marie Sklodowska-Curie Innovative Training Network European Industrial Doctorate grant agreement No. 956832 “Advanced machine learning for Innovative Drug Discovery” (AIDD), Horizon Europe Marie Skłodowska-Curie Actions Doctoral Network grant agreement No. 101120466 “Explainable AI for Molecules” (AiChemist) as well as by Chemical Research in Toxicology and ICANN2024.

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Our Approach:

• Advanced Graph Neural Networks – Proprietary architectures tailored for molecular data

• Multitask Learning – Leveraging correlated endpoints for improved accuracy

• Bioactivity-Informed Descriptors – Enriching models with experimental context

• Consensus Modeling – Combining strengths of multiple predictors for robust results

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Results: Shared 2nd Place among 78 groups and companies worldwide

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References:

• Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data. Cirino et al., DOI: 10.1021/acs.chemrestox.5c00018

• Which modern AI methods provide accurate predictions of toxicological endpoints? Analysis of Tox24 challenge results. Eytcheson et al., DOI: 10.1021/acs.chemrestox.5c00273