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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.
HAGCN: Heterogeneous Attentive GCN for Gene-Disease Association
http://doi.org/10.5626/JOK.2025.52.2.161
Predicting gene-disease associations (GDAs) is essential for understanding molecular mechanisms, diagnosing disease, and targeting genes. Validating causal relationships between diseases and genes using experimental methods can be extremely costly and time-consuming. Deep learning, particularly graph neural networks, has shown great promise in this area. However, most models rely on single-source, homogeneous graphs. Another is the need for expert knowledge in manual definition of meta-paths to build multi-source heterogeneous graphs. Recognizing these challenges, the present study introduces the Heterogeneous Attentive Graph Convolution Network (HAGCN). HAGCN processes heterogeneous biological entity association graphs as input. We construct the input graphs using the biological association information from curated databases such as Gene Ontology, Disease Ontology, Human Phenotype Ontology, and TBGA. HAGCN learns the relationship heterogeneity between biological entities without meta-paths by using the attention mechanism. HAGCN achieved the best performance in AUC-ROC in a binary classification task to predict gene-disease association, and also achieved competitive performance in F1 score, MCC, and accuracy against baselines. We believe that HAGCN can accelerate the discovery of disease-associated genes and
Predicting Chemical Structure of Drugs Using Deep Learning
Soohyun Ko, Chihyun Park, Jaegyoon Ahn
http://doi.org/10.5626/JOK.2021.48.2.234
Numerous computer-based methods have been investigated in attempts to reduce the time and cost of drug development. In particular, with the recent development of deep learning techniques, various generation models for generating the chemical formulas of candidate compounds and reinforcement learning models to generate chemical formulas that satisfy specific conditions have been presented. In this paper, we propose a reinforcement learning model that exploits predicted binding affinity information between specific proteins and generated compounds. More specifically, the generative model used in this paper is Stack-RNN, and reinforcement learning is implemented by using Stack-RNN as a policy to ensure that the generated formula has specific chemical properties and high binding affinity with specific proteins. The proposed model generates paper, we generated the chemical formulas of compounds that are similar to three anti-cancer drugs (Sorafenib, Sunitinib, and Dasatinib) by using the target protein information of these three anti-cancer drugs.
Prediction of Compound-Protein Interactions Using Deep Learning
http://doi.org/10.5626/JOK.2019.46.10.1054
Characterizing the interactions between compounds and proteins is an important process for drug development and discovery. Structural data of proteins and compounds are used to identify their interactions, but those structural data are not always available, and the speed and accuracy of the predictions made in this way ware limited due to the large number of calculations involved. In this paper, compound-protein interactions were predicted using S2SAE (Sequence-To-Sequence Auto-Encoder), which is composed of a sequence-to-sequence algorithm used in machine translation as well as an auto-encoder for effective compression of the input vector. Compared to the existing method, the method proposed in this paper uses fewer features of protein-compound complex and also show higher predictive accuracy.
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