Multi-View Self-Attention for Interpretable Drug-Target Interaction Prediction

• URL: http://arxiv.org/abs/2005.00397v2
• Date: Sun, 23 Aug 2020 14:49:47 GMT
• Title: Multi-View Self-Attention for Interpretable Drug-Target Interaction Prediction
• Authors: Brighter Agyemang and Wei-Ping Wu and Michael Yelpengne Kpiebaareh and Zhihua Lei and Ebenezer Nanor and Lei Chen
• Abstract summary: In machine learning approaches, the numerical representation of molecules is critical to the performance of the model. We propose a self-attention-based multi-view representation learning approach for modeling drug-target interactions.
• Score: 4.307720252429733
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The drug discovery stage is a vital aspect of the drug development process and forms part of the initial stages of the development pipeline. In recent times, machine learning-based methods are actively being used to model drug-target interactions for rational drug discovery due to the successful application of these methods in other domains. In machine learning approaches, the numerical representation of molecules is critical to the performance of the model. While significant progress has been made in molecular representation engineering, this has resulted in several descriptors for both targets and compounds. Also, the interpretability of model predictions is a vital feature that could have several pharmacological applications. In this study, we propose a self-attention-based multi-view representation learning approach for modeling drug-target interactions. We evaluated our approach using three benchmark kinase datasets and compared the proposed method to some baseline models. Our experimental results demonstrate the ability of our method to achieve competitive prediction performance and offer biologically plausible drug-target interaction interpretations.

Related papers

• Large Language Models in Drug Discovery and Development: From Disease Mechanisms to Clinical Trials [49.19897427783105]
  The integration of Large Language Models (LLMs) into the drug discovery and development field marks a significant paradigm shift. We investigate how these advanced computational models can uncover target-disease linkage, interpret complex biomedical data, enhance drug molecule design, predict drug efficacy and safety profiles, and facilitate clinical trial processes.
  arXiv  Detail & Related papers  (2024-09-06T02:03:38Z)
• Evaluation Framework for AI-driven Molecular Design of Multi-target Drugs: Brain Diseases as a Case Study [0.0]
  Multi-target Drug Discovery (MTDD) is an emerging paradigm for discovering drugs against complex disorders. This work proposes an evaluation framework for molecule generation techniques in MTDD scenarios.
  arXiv  Detail & Related papers  (2024-08-20T01:42:16Z)
• Physical formula enhanced multi-task learning for pharmacokinetics prediction [54.13787789006417]
  A major challenge for AI-driven drug discovery is the scarcity of high-quality data. We develop a formula enhanced mul-ti-task learning (PEMAL) method that predicts four key parameters of pharmacokinetics simultaneously. Our experiments reveal that PEMAL significantly lowers the data demand, compared to typical Graph Neural Networks.
  arXiv  Detail & Related papers  (2024-04-16T07:42:55Z)
• Drug Synergistic Combinations Predictions via Large-Scale Pre-Training and Graph Structure Learning [82.93806087715507]
  Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. Deep learning models have emerged as an efficient way to discover synergistic combinations. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods.
  arXiv  Detail & Related papers  (2023-01-14T15:07:43Z)
• A biologically-inspired evaluation of molecular generative machine learning [17.623886600638716]
  A novel biologically-inspired benchmark for the evaluation of molecular generative models is proposed. We propose a recreation metric, apply drug-target affinity prediction and molecular docking as complementary techniques for the evaluation of generative outputs.
  arXiv  Detail & Related papers  (2022-08-20T11:01:10Z)
• SSM-DTA: Breaking the Barriers of Data Scarcity in Drug-Target Affinity Prediction [127.43571146741984]
  Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery. wet experiments remain the most reliable method, but they are time-consuming and resource-intensive. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. We present the SSM-DTA framework, which incorporates three simple yet highly effective strategies.
  arXiv  Detail & Related papers  (2022-06-20T14:53:25Z)
• Associative Learning Mechanism for Drug-Target Interaction Prediction [6.107658437700639]
  Drug-target affinity (DTA) represents the strength of drug-target interaction (DTI) Traditional methods lack the interpretability of the DTA prediction process. This paper proposes a DTA prediction method with interactive learning and an autoencoder mechanism.
  arXiv  Detail & Related papers  (2022-05-24T14:25:28Z)
• A Unified View of Relational Deep Learning for Polypharmacy Side Effect, Combination Synergy, and Drug-Drug Interaction Prediction [6.214548392474976]
  In recent years, numerous machine learning models which attempt to solve polypharmacy side effect identification, drug-drug interaction prediction and combination therapy design tasks have been proposed. Here, we present a unified theoretical view of relational machine learning models which can address these tasks.
  arXiv  Detail & Related papers  (2021-11-04T14:48:48Z)
• Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer [98.8319016075089]
  We propose a novel approach to model intermolecular information with a three-way Transformer-based architecture. Intermolecular Graph Transformer (IGT) outperforms state-of-the-art approaches by 9.1% and 20.5% over the second best for binding activity and binding pose prediction respectively. IGT exhibits promising drug screening ability against SARS-CoV-2 by identifying 83.1% active drugs that have been validated by wet-lab experiments with near-native predicted binding poses.
  arXiv  Detail & Related papers  (2021-10-14T13:28:02Z)
• Graph2MDA: a multi-modal variational graph embedding model for predicting microbe-drug associations [7.149873402253933]
  Microbes have become novel targets for the development of antibacterial agents. screening of microbe-drug associations can benefit greatly drug research and development. We propose a novel method, Graph2MDA, to predict microbe-drug associations.
  arXiv  Detail & Related papers  (2021-08-14T07:33:05Z)
• Multi-Agent Imitation Learning with Copulas [102.27052968901894]
  Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions. In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems. Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents.
  arXiv  Detail & Related papers  (2021-07-10T03:49:41Z)

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.