From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning

• URL: http://arxiv.org/abs/2208.10230v4
• Date: Mon, 2 Sep 2024 07:10:37 GMT
• Title: From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning
• Authors: Yaosen Min, Ye Wei, Peizhuo Wang, Xiaoting Wang, Han Li, Nian Wu, Stefan Bauer, Shuxin Zheng, Yu Shi, Yingheng Wang, Ji Wu, Dan Zhao, Jianyang Zeng,
• Abstract summary: Dynaformer is a graph-based deep learning model developed to predict protein-ligand binding affinities. It exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset. In a virtual screening on heat shock protein 90 (HSP90), 20 candidates are identified and their binding affinities are experimentally validated.
• Score: 40.83037811977803
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only take advantage of static crystal structures while the actual binding affinities are generally determined by the thermodynamic ensembles between proteins and ligands. One effective way to approximate such a thermodynamic ensemble is to use molecular dynamics (MD) simulation. Here, an MD dataset containing 3,218 different protein-ligand complexes is curated, and Dynaformer, a graph-based deep learning model is further developed to predict the binding affinities by learning the geometric characteristics of the protein-ligand interactions from the MD trajectories. In silico experiments demonstrated that the model exhibits state-of-the-art scoring and ranking power on the CASF-2016 benchmark dataset, outperforming the methods hitherto reported. Moreover, in a virtual screening on heat shock protein 90 (HSP90) using Dynaformer, 20 candidates are identified and their binding affinities are further experimentally validated. Dynaformer displayed promising results in virtual drug screening, revealing 12 hit compounds (two are in the submicromolar range), including several novel scaffolds. Overall, these results demonstrated that the approach offer a promising avenue for accelerating the early drug discovery process.

Related papers

• BAPULM: Binding Affinity Prediction using Language Models [7.136205674624813]
  We introduce BAPULM, an innovative sequence-based framework that leverages the chemical latent representations of proteins via ProtT5-XL-U50 and through MolFormer. Our approach was validated extensively on benchmark datasets, achieving sequential scoring power (R) values of 0.925 $pm$ 0.043, 0.914 $pm$ 0.004, and 0.8132 $pm$ 0.001 on benchmark1k2101, Test2016_290, and CSAR-HiQ_36, respectively.
  arXiv  Detail & Related papers  (2024-11-06T04:35:30Z)
• Pre-trained Molecular Language Models with Random Functional Group Masking [54.900360309677794]
  We propose a SMILES-based underlineem Molecular underlineem Language underlineem Model, which randomly masking SMILES subsequences corresponding to specific molecular atoms. This technique aims to compel the model to better infer molecular structures and properties, thus enhancing its predictive capabilities.
  arXiv  Detail & Related papers  (2024-11-03T01:56:15Z)
• SPIN: SE(3)-Invariant Physics Informed Network for Binding Affinity Prediction [3.406882192023597]
  Accurate prediction of protein-ligand binding affinity is crucial for drug development. Traditional methods often fail to accurately model the complex's spatial information. We propose SPIN, a model that incorporates various inductive biases applicable to this task.
  arXiv  Detail & Related papers  (2024-07-10T08:40:07Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [74.93549765488103]
  In drug discovery, molecular dynamics simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding. We show the efficiency and effectiveness of NeuralMD, with a 2000$times$ speedup over standard numerical MD simulation and outperforming all other ML approaches by up to 80% under the stability metric.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction [1.534667887016089]
  We present a novel method for generating molecular fingerprints using multi parameter persistent homology (MPPH) This technique holds considerable significance for drug discovery and materials science, where precise molecular property prediction is vital. We demonstrate its superior performance over existing state-of-the-art methods in predicting molecular properties through extensive evaluations on the MoleculeNet benchmark.
  arXiv  Detail & Related papers  (2023-12-12T09:33:54Z)
• PIGNet2: A Versatile Deep Learning-based Protein-Ligand Interaction Prediction Model for Binding Affinity Scoring and Virtual Screening [0.0]
  Prediction of protein-ligand interactions (PLI) plays a crucial role in drug discovery. The development of a versatile model capable of accurately scoring binding affinity and conducting efficient virtual screening remains a challenge. Here, we propose a viable solution by introducing a novel data augmentation strategy combined with a physics-informed graph neural network.
  arXiv  Detail & Related papers  (2023-07-03T14:46:49Z)
• State-specific protein-ligand complex structure prediction with a multi-scale deep generative model [68.28309982199902]
  We present NeuralPLexer, a computational approach that can directly predict protein-ligand complex structures. Our study suggests that a data-driven approach can capture the structural cooperativity between proteins and small molecules, showing promise in accelerating the design of enzymes, drug molecules, and beyond.
  arXiv  Detail & Related papers  (2022-09-30T01:46:38Z)
• Widely Used and Fast De Novo Drug Design by a Protein Sequence-Based Reinforcement Learning Model [4.815696666006742]
  Structure-based de novo method can overcome the data scarcity of active by incorporating drug-target interaction into deep generative architectures. Here, we demonstrate a widely used and fast protein sequence-based reinforcement learning model for drug discovery. As a proof of concept, the RL model was utilized to design molecules for four targets.
  arXiv  Detail & Related papers  (2022-08-14T10:41:52Z)
• 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)
• Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts [80.69440684790925]
  DeepRelations is a physics-inspired deep relational network with intrinsically explainable architecture. It shows superior interpretability to the state-of-the-art. It boosts the AUPRC of contact prediction 9.5, 16.9, 19.3 and 5.7-fold for the test, compound-unique, protein-unique, and both-unique sets.
  arXiv  Detail & Related papers  (2019-12-29T00:14:07Z)

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.