Bridging the gap between target-based and cell-based drug discovery with a graph generative multi-task model

• URL: http://arxiv.org/abs/2208.04944v1
• Date: Tue, 9 Aug 2022 02:35:42 GMT
• Title: Bridging the gap between target-based and cell-based drug discovery with a graph generative multi-task model
• Authors: Fan Hu, Dongqi Wang, Huazhen Huang, Yishen Hu and Peng Yin
• Abstract summary: Drug discovery is vitally important for protecting human against disease. Target-based screening is one of the most popular methods to develop new drugs in the past several decades. This method efficiently screens candidate drugs inhibiting target protein in vitro, but it often fails due to inadequate activity in vivo. We propose a novel graph multi task deep learning model to identify compounds carrying both target inhibitory and cell active properties.
• Score: 4.917317902787792
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Drug discovery is vitally important for protecting human against disease. Target-based screening is one of the most popular methods to develop new drugs in the past several decades. This method efficiently screens candidate drugs inhibiting target protein in vitro, but it often fails due to inadequate activity of the selected drugs in vivo. Accurate computational methods are needed to bridge this gap. Here, we propose a novel graph multi task deep learning model to identify compounds carrying both target inhibitory and cell active (MATIC) properties. On a carefully curated SARS-CoV-2 dataset, the proposed MATIC model shows advantages comparing with traditional method in screening effective compounds in vivo. Next, we explored the model interpretability and found that the learned features for target inhibition (in vitro) or cell active (in vivo) tasks are different with molecular property correlations and atom functional attentions. Based on these findings, we utilized a monte carlo based reinforcement learning generative model to generate novel multi-property compounds with both in vitro and in vivo efficacy, thus bridging the gap between target-based and cell-based drug discovery.

Related papers

• MOTIVE: A Drug-Target Interaction Graph For Inductive Link Prediction [0.29998889086656577]
  This paper introduces MOTIVE, a Morphological cOmpound Target Interaction Graph dataset comprising Cell Painting features for 11,000 genes and 3,600 compounds. We provide random, cold-source (new drugs) and cold-target (new genes) data splits to enable rigorous evaluation under realistic use cases. Our benchmark results show that graph neural networks that use Cell Painting features consistently outperform those that learn from graph structure alone.
  arXiv  Detail & Related papers  (2024-06-12T21:18:14Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• PGraphDTA: Improving Drug Target Interaction Prediction using Protein Language Models and Contact Maps [4.590060921188914]
  Key aspect of drug discovery involves identifying novel drug-target (DT) interactions. Protein-ligand interactions exhibit a continuum of binding strengths, known as binding affinity. We propose novel enhancements to enhance their performance.
  arXiv  Detail & Related papers  (2023-10-06T05:00:25Z)
• 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)
• Drug-target affinity prediction method based on consistent expression of heterogeneous data [0.0]
  We propose a method for predicting drug-target binding affinity using deep learning models. The proposed model demonstrates its accuracy and effectiveness in predicting drug-target binding affinity on the DAVIS and KIBA datasets.
  arXiv  Detail & Related papers  (2022-11-13T02:58:03Z)
• 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)
• 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)
• Learning Neural Causal Models with Active Interventions [83.44636110899742]
  We introduce an active intervention-targeting mechanism which enables a quick identification of the underlying causal structure of the data-generating process. Our method significantly reduces the required number of interactions compared with random intervention targeting. We demonstrate superior performance on multiple benchmarks from simulated to real-world data.
  arXiv  Detail & Related papers  (2021-09-06T13:10:37Z)
• 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)
• Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics [109.70543391923344]
  CLaSS (Controlled Latent attribute Space Sampling) is an efficient computational method for attribute-controlled generation of molecules. We screen the generated molecules for additional key attributes by using deep learning classifiers in conjunction with novel features derived from atomistic simulations. The proposed approach is demonstrated for designing non-toxic antimicrobial peptides (AMPs) with strong broad-spectrum potency.
  arXiv  Detail & Related papers  (2020-05-22T15:57:58Z)
• Multi-View Self-Attention for Interpretable Drug-Target Interaction Prediction [4.307720252429733]
  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.
  arXiv  Detail & Related papers  (2020-05-01T14:28:17Z)

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.