PUBLICATIONS

Our team won a shared second place in the Tox24 Challenge out of 80 participants

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: 10.26434/chemrxiv-2025-7k7x3

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Our recent publications

BARTSmiles: Generative Masked Language Models for Molecular Representations

Chilingaryan et al., Machine Learning; Biomolecules: 2022., DOI: 10.48550/arXiv.2211.16349

Improving VAE based molecular representations for compound property prediction

Tevosyan et al., J Cheminform. 2022 Oct 14;14(1):69., DOI: 10.1186/s13321-022-00648-x​

Enhancing Chemical-Induced Human Carcinogenic Risk Evaluation through Advanced AI Technologies,

Babayan et al, DOI: 10.3390/proceedings2024102012​

AI/ML modeling to enhance the capability of in vitro and in vivo tests in predicting human carcinogenicity,

Tevosyan et al., DOI: 10.1016/j.mrgentox.2025.503858

Predictive, integrative, and regulatory aspects of AI-driven computational toxicology – Highlights of the German Pharm-Tox Summit (GPTS) 2024,

Haßmann et al., DOI: 10.1016/j.tox.2024.153975

Datasets Construction and Development of QSAR Models for Predicting Micronucleus In Vitro and In Vivo Assay Outcomes

Khondkaryan et al,. Toxics 2023, 11, 785. DOI: 10.3390/toxics11090785

 

Other articles

Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World

Seal et al., Chemical Research in Toxicology, 2025, 38, 5, 759–807, DOI: 10.1021/acs.chemrestox.5c00033

Applications of machine learning in drug discovery and development

Vamathevan et al., Nat Rev Drug Discov 18, 463–477 (2019). DOI: 10.1038/s41573-019-0024-5

​Non-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., Med Biol Res. 2016; 49(12): e5646. 

​A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds

He at al., Front. Oncol., 22 July, 2021, DOI: 10.3389/fonc.2021.711225

Editorial: In silico Methods for Drug Design and Discovery

Brogi et al., Front. Chem., 07 August 2020, DOI: 10.3389/fchem.2020.00612

Graph convolutional networks for computational drug development and discovery

Sun et al., Brief Bioinform​. 2020 May 21;21(3):919-935. DOI: 10.1093/bib/bbz042.

​Drug discovery with explainable artificial intelligence

Jiménez-Luna et al., Nat Mach Intell 2, 573–584 (2020). DOI: 10.1038/s42256-020-00236-4

​Predicting Toxicity Properties through Machine Learning

Borrero et al., Procedia Computer Science, Volume 170, 2020, Pages 1011-1016, DOI: 10.1016/j.procs.2020.03.093