Published research
behind the platform
Toxometris predictions are backed by peer-reviewed science. Our team publishes openly on the methods, datasets, and models that power the platform.
Toxometris Team Publications
7 publications
BARTSmiles: Generative Masked Language Models for Molecular Representations
Chilingaryan et al.
ACS Journal of Chemical Information and Modeling
10.1021/acs.jcim.4c00512Improving VAE based molecular representations for compound property prediction
Tevosyan et al.
Journal of Cheminformatics
10.1186/s13321-022-00648-xEnhancing Chemical-Induced Human Carcinogenic Risk Evaluation through Advanced AI Technologies
Babayan et al.
MDPI Proceedings
10.3390/proceedings2024102012AI/ML modeling to enhance the capability of in vitro and in vivo tests in predicting human carcinogenicity
Tevosyan et al.
Mutation Research
10.1016/j.mrgentox.2025.503858Predictive, integrative, and regulatory aspects of AI-driven computational toxicology – Highlights of the German Pharm-Tox Summit (GPTS) 2024
Haßmann et al.
Toxicology
10.1016/j.tox.2024.153975Datasets Construction and Development of QSAR Models for Predicting Micronucleus In Vitro and In Vivo Assay Outcomes
Khondkaryan et al.
Toxics
10.3390/toxics11090785Synthesis, in silico, and in vitro pharmacological evaluation of norbornenylpiperazine derivatives as potential ligands for nuclear hormone receptors
Badalyan et al.
Journal of Applied Pharmaceutical Sciences
10.7324/JAPS.2025.230239Tox24 Challenge Publications
2 publications · Independent evaluation of our models
Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data
Cirino et al.
ACS Chemical Research in Toxicology
10.1021/acs.chemrestox.5c00018Which modern AI methods provide accurate predictions of toxicological endpoints? Analysis of Tox24 challenge results
Eytcheson et al.
ACS Chemical Research in Toxicology
10.1021/acs.chemrestox.5c00273Referenced Articles
8 articles · Key literature cited in our work
Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World
Seal et al. · 2025
Chemical Research in Toxicology
10.1021/acs.chemrestox.5c00033Applications of machine learning in drug discovery and development
Vamathevan et al. · 2019
Nature Reviews Drug Discovery
10.1038/s41573-019-0024-5Non-clinical studies in the process of new drug development — Part II: Good laboratory practice, metabolism, pharmacokinetics, safety and dose translation to clinical studies
Andrade et al. · 2016
Medical and Biological Research
PMC5188860A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds
He et al. · 2021
Frontiers in Oncology
10.3389/fonc.2021.711225Editorial: In silico Methods for Drug Design and Discovery
Brogi et al. · 2020
Frontiers in Chemistry
10.3389/fchem.2020.00612Graph convolutional networks for computational drug development and discovery
Sun et al. · 2020
Briefings in Bioinformatics
10.1093/bib/bbz042Drug discovery with explainable artificial intelligence
Jiménez-Luna et al. · 2020
Nature Machine Intelligence
10.1038/s42256-020-00236-4Predicting Toxicity Properties through Machine Learning
Borrero et al. · 2020
Procedia Computer Science
10.1016/j.procs.2020.03.093