[2408.05696] SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction

Computer Science > Machine Learning arXiv:2408.05696 (cs) [Submitted on 11 Aug 2024 (v1), last revised 26 Mar 2026 (this version, v2)] Title:SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction Authors:Bohao Xu, Yingzhou Lu, Chenhao Li, Ling Yue, Xiao Wang, Tianfan Fu, Minjie Shen, Lulu Chen View a PDF of the paper titled SMILES-Mamba: Chemical Mamba Foundation Models for Drug ADMET Prediction, by Bohao Xu and 7 other authors View PDF Abstract:In drug discovery, predicting the absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of small-molecule drugs is critical for ensuring safety and efficacy. However, the process of accurately predicting these properties is often resource-intensive and requires extensive experimental data. To address this challenge, we propose SMILES-Mamba, a two-stage model that leverages both unlabeled and labeled data through a combination of self-supervised pretraining and fine-tuning strategies. The model first pre-trains on a large corpus of unlabeled SMILES strings to capture the underlying chemical structure and relationships, before being fine-tuned on smaller, labeled datasets specific to ADMET tasks. Our results demonstrate that SMILES-Mamba exhibits competitive performance across 22 ADMET datasets, achieving the highest score in 14 tasks, highlighting the potential of self-supervised learning in improving molecular property prediction. This approach not only enhances prediction accuracy but a...