Toward Robust Drug-Target Interaction Prediction via Ensemble Modeling and Transfer Learning

• URL: http://arxiv.org/abs/2107.00719v1
• Date: Fri, 2 Jul 2021 04:00:03 GMT
• Title: Toward Robust Drug-Target Interaction Prediction via Ensemble Modeling and Transfer Learning
• Authors: Po-Yu Kao, Shu-Min Kao, Nan-Lan Huang, Yen-Chu Lin
• Abstract summary: We introduce an ensemble of deep learning models (EnsembleDLM) for robust DTI prediction. EnsembleDLM only uses the sequence information of chemical compounds and proteins, and it aggregates the predictions from multiple deep neural networks. It achieves state-of-the-art performance in Davis and KIBA datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Drug-target interaction (DTI) prediction plays a crucial role in drug discovery, and deep learning approaches have achieved state-of-the-art performance in this field. We introduce an ensemble of deep learning models (EnsembleDLM) for robust DTI prediction. EnsembleDLM only uses the sequence information of chemical compounds and proteins, and it aggregates the predictions from multiple deep neural networks. This approach reduces the chance of overfitting, yields an unbiased prediction, and achieves state-of-the-art performance in Davis and KIBA datasets. EnsembleDLM also reaches state-of-the-art performance in cross-domain applications and decent cross-domain performance (Pearson correlation coefficient and concordance index > 0.8) with transfer learning using approximately twice the amount of test data in the new domain.

Related papers

• SCOPE-DTI: Semi-Inductive Dataset Construction and Framework Optimization for Practical Usability Enhancement in Deep Learning-Based Drug Target Interaction Prediction [15.996998422919111]
  SCOPE-DTI is a unified framework combining a large-scale, balanced semi-inductive human DTI dataset with advanced deep learning modeling. The SCOPE dataset expands data volume by up to 100-fold compared to common benchmarks such as the Human dataset. By offering comprehensive data, advanced modeling, and accessible tools, SCOPE-DTI accelerates drug discovery research.
  arXiv  Detail & Related papers  (2025-03-12T10:46:25Z)
• Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks [1.6112718683989882]
  This study presents a novel approach that applies transfer learning from AC prediction to enhance DTI prediction. Unlike previous studies, which treat AC and DTI predictions as separate problems, this work establishes a unified framework to address both data scarcity and prediction challenges in drug discovery.
  arXiv  Detail & Related papers  (2024-12-11T18:56:11Z)
• MIN: Multi-channel Interaction Network for Drug-Target Interaction with Protein Distillation [64.4838301776267]
  Multi-channel Interaction Network (MIN) is a novel framework designed to predict drug-target interaction (DTI) MIN incorporates a representation learning module and a multi-channel interaction module. MIN is not only a potent tool for DTI prediction but also offers fresh insights into the prediction of protein binding sites.
  arXiv  Detail & Related papers  (2024-11-23T05:38:36Z)
• On Machine Learning Approaches for Protein-Ligand Binding Affinity Prediction [2.874893537471256]
  This study evaluates the performance of classical tree-based models and advanced neural networks in protein-ligand binding affinity prediction. We show that combining 2D and 3D model strengths improves active learning outcomes beyond current state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-07-15T13:06:00Z)
• Benchmark on Drug Target Interaction Modeling from a Structure Perspective [48.60648369785105]
  Drug-target interaction prediction is crucial to drug discovery and design. Recent methods, such as those based on graph neural networks (GNNs) and Transformers, demonstrate exceptional performance across various datasets. We conduct a comprehensive survey and benchmark for drug-target interaction modeling from a structure perspective, via integrating tens of explicit (i.e., GNN-based) and implicit (i.e., Transformer-based) structure learning algorithms.
  arXiv  Detail & Related papers  (2024-07-04T16:56:59Z)
• A Cross-Field Fusion Strategy for Drug-Target Interaction Prediction [85.2792480737546]
  Existing methods fail to utilize global protein information during DTI prediction. Cross-field information fusion strategy is employed to acquire local and global protein information. Siamese drug-target interaction SiamDTI prediction method achieves higher accuracy levels than other state-of-the-art (SOTA) methods on novel drugs and targets.
  arXiv  Detail & Related papers  (2024-05-23T13:25:20Z)
• ResDTA: Predicting Drug-Target Binding Affinity Using Residual Skip Connections [0.0]
  We present a deep learning-based methodology for predicting DT binding affinities using just sequencing information from both targets and drugs. The proposed model achieves the best Concordance Index (CI) performance in one of the largest benchmark datasets.
  arXiv  Detail & Related papers  (2023-03-20T20:27:11Z)
• Domain Adaptation with Adversarial Training on Penultimate Activations [82.9977759320565]
  Enhancing model prediction confidence on unlabeled target data is an important objective in Unsupervised Domain Adaptation (UDA) We show that this strategy is more efficient and better correlated with the objective of boosting prediction confidence than adversarial training on input images or intermediate features.
  arXiv  Detail & Related papers  (2022-08-26T19:50:46Z)
• Interpretable bilinear attention network with domain adaptation improves drug-target prediction [4.15790071124993]
  DrugBAN is a deep bilinear attention network framework with domain adaptation to learn pair-wise local interactions between drugs and targets. DrugBAN works on drug molecular graphs and target protein sequences to perform prediction. Experiments on three benchmark datasets show that DrugBAN achieves the best overall performance against five state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-08-03T16:27:29Z)
• Multiple Similarity Drug-Target Interaction Prediction with Random Walks and Matrix Factorization [16.41618129467975]
  We take a multi-layered network perspective, where different layers correspond to different similarity metrics between drugs and targets. To fully take advantage of topology information captured in multiple views, we develop an optimization framework, called MDMF, for DTI prediction. The framework learns vector representations of drugs and targets that not only retain higher-order proximity across all hyper-layers, but also approximates the interactions with their inner product.
  arXiv  Detail & Related papers  (2022-01-24T08:02:05Z)
• PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning [109.84770951839289]
  We present PredRNN, a new recurrent network for learning visual dynamics from historical context. We show that our approach obtains highly competitive results on three standard datasets.
  arXiv  Detail & Related papers  (2021-03-17T08:28:30Z)
• A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning [72.30054522048553]
  We present a new method, Transductive Multi-Head Few-Shot learning (TMHFS), to address the Cross-Domain Few-Shot Learning challenge. The proposed methods greatly outperform the strong baseline, fine-tuning, on four different target domains.
  arXiv  Detail & Related papers  (2020-06-08T02:39:59Z)
• Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response [49.86828302591469]
  In this paper, we apply transfer learning to the prediction of anti-cancer drug response. We apply the classic transfer learning framework that trains a prediction model on the source dataset and refines it on the target dataset. The ensemble transfer learning pipeline is implemented using LightGBM and two deep neural network (DNN) models with different architectures.
  arXiv  Detail & Related papers  (2020-05-13T20:29:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.