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A Pretrained Model-Based Approach to Improve Generalization Performance for ADMET Prediction of Drug Candidates
http://doi.org/10.5626/JOK.2025.52.7.601
Accurate prediction of Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties plays an important role in reducing clinical trial failure rates and lowering drug development costs. In this study, we propose a novel method to improve ADMET prediction performance for drug candidate compounds by integrating molecular embeddings from a graph transformer model with pretrained embeddings from a UniMol model. The proposed model can capture bond type information from molecular graph structures, generating chemically refined representations, while leveraging UniMol’s pretrained 3D embeddings to effectively learn spatial molecular characteristics. Through this, the model is designed to address the problem of data scarcity and enhance the generalization performance. In this study, we conducted prediction experiments on 10 ADMET properties. The experiment results demonstrated that our proposed model outperformed existing methods and that the prediction accuracy for ADMET properties could be improved by effectively integrating atomic bond information and 3D structures.
Prediction of Dehydrogenation Enthalpy Using Graph Isomorphism Network
Kun Young Choi, Woo Hyun Yuk, Jeong Woo Han, Cham Kill Hong
http://doi.org/10.5626/JOK.2024.51.5.406
This paper conducts dehydrogenation enthalpy prediction that could play an important role in selecting optimal liquid organic hydrogen carriers. We employed graph convolutional networks, which produced molecular embeddings for the prediction. Specifically, we adopted Graph Isomorphism Network (GIN) known to be the most expressive graph-based representation learning model. Our proposed approach could build molecular embeddings. Our proposed approach outperformed conventional machine learning solutions and traditional representations based on chemical physics algorithms. In addition, the performance of the proposed model could be improved with small batch sizes and deeper GCN layers using skip connections.
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