Improved Protein-ligand Binding Affinity Prediction with Structure-Based
Deep Fusion Inference
- URL: http://arxiv.org/abs/2005.07704v1
- Date: Sun, 17 May 2020 22:26:27 GMT
- Title: Improved Protein-ligand Binding Affinity Prediction with Structure-Based
Deep Fusion Inference
- Authors: Derek Jones, Hyojin Kim, Xiaohua Zhang, Adam Zemla, Garrett Stevenson,
William D. Bennett, Dan Kirshner, Sergio Wong, Felice Lightstone and Jonathan
E. Allen
- Abstract summary: Predicting accurate protein-ligand binding affinity is important in drug discovery.
Recent advances in the deep convolutional and graph neural network based approaches, the model performance depends on the input data representation.
We present fusion models to benefit from different feature representations of two neural network models to improve the binding affinity prediction.
- Score: 3.761791311908692
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Predicting accurate protein-ligand binding affinity is important in drug
discovery but remains a challenge even with computationally expensive
biophysics-based energy scoring methods and state-of-the-art deep learning
approaches. Despite the recent advances in the deep convolutional and graph
neural network based approaches, the model performance depends on the input
data representation and suffers from distinct limitations. It is natural to
combine complementary features and their inference from the individual models
for better predictions. We present fusion models to benefit from different
feature representations of two neural network models to improve the binding
affinity prediction. We demonstrate effectiveness of the proposed approach by
performing experiments with the PDBBind 2016 dataset and its docking pose
complexes. The results show that the proposed approach improves the overall
prediction compared to the individual neural network models with greater
computational efficiency than related biophysics based energy scoring
functions. We also discuss the benefit of the proposed fusion inference with
several example complexes. The software is made available as open source at
https://github.com/llnl/fast.
Related papers
- Discovering Physics-Informed Neural Networks Model for Solving Partial Differential Equations through Evolutionary Computation [5.8407437499182935]
This article proposes an evolutionary computation method aimed at discovering the PINNs model with higher approximation accuracy and faster convergence rate.
In experiments, the performance of different models that are searched through Bayesian optimization, random search and evolution is compared in solving Klein-Gordon, Burgers, and Lam'e equations.
arXiv Detail & Related papers (2024-05-18T07:32:02Z) - Analyzing Neural Network-Based Generative Diffusion Models through Convex Optimization [45.72323731094864]
We present a theoretical framework to analyze two-layer neural network-based diffusion models.
We prove that training shallow neural networks for score prediction can be done by solving a single convex program.
Our results provide a precise characterization of what neural network-based diffusion models learn in non-asymptotic settings.
arXiv Detail & Related papers (2024-02-03T00:20:25Z) - FABind: Fast and Accurate Protein-Ligand Binding [127.7790493202716]
$mathbfFABind$ is an end-to-end model that combines pocket prediction and docking to achieve accurate and fast protein-ligand binding.
Our proposed model demonstrates strong advantages in terms of effectiveness and efficiency compared to existing methods.
arXiv Detail & Related papers (2023-10-10T16:39:47Z) - Physics Inspired Hybrid Attention for SAR Target Recognition [61.01086031364307]
We propose a physics inspired hybrid attention (PIHA) mechanism and the once-for-all (OFA) evaluation protocol to address the issues.
PIHA leverages the high-level semantics of physical information to activate and guide the feature group aware of local semantics of target.
Our method outperforms other state-of-the-art approaches in 12 test scenarios with same ASC parameters.
arXiv Detail & Related papers (2023-09-27T14:39:41Z) - Structured Radial Basis Function Network: Modelling Diversity for
Multiple Hypotheses Prediction [51.82628081279621]
Multi-modal regression is important in forecasting nonstationary processes or with a complex mixture of distributions.
A Structured Radial Basis Function Network is presented as an ensemble of multiple hypotheses predictors for regression problems.
It is proved that this structured model can efficiently interpolate this tessellation and approximate the multiple hypotheses target distribution.
arXiv Detail & Related papers (2023-09-02T01:27:53Z) - 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) - Predicting Biomedical Interactions with Probabilistic Model Selection
for Graph Neural Networks [5.156812030122437]
Current biological networks are noisy, sparse, and incomplete. Experimental identification of such interactions is both time-consuming and expensive.
Deep graph neural networks have shown their effectiveness in modeling graph-structured data and achieved good performance in biomedical interaction prediction.
Our proposed method enables the graph convolutional networks to dynamically adapt their depths to accommodate an increasing number of interactions.
arXiv Detail & Related papers (2022-11-22T20:44:28Z) - HyperImpute: Generalized Iterative Imputation with Automatic Model
Selection [77.86861638371926]
We propose a generalized iterative imputation framework for adaptively and automatically configuring column-wise models.
We provide a concrete implementation with out-of-the-box learners, simulators, and interfaces.
arXiv Detail & Related papers (2022-06-15T19:10:35Z) - Structure-aware Interactive Graph Neural Networks for the Prediction of
Protein-Ligand Binding Affinity [52.67037774136973]
Drug discovery often relies on the successful prediction of protein-ligand binding affinity.
Recent advances have shown great promise in applying graph neural networks (GNNs) for better affinity prediction by learning the representations of protein-ligand complexes.
We propose a structure-aware interactive graph neural network (SIGN) which consists of two components: polar-inspired graph attention layers (PGAL) and pairwise interactive pooling (PiPool)
arXiv Detail & Related papers (2021-07-21T03:34:09Z) - PIGNet: A physics-informed deep learning model toward generalized
drug-target interaction predictions [0.0]
We propose two key strategies to enhance generalization in the DTI model.
The first is to predict the atom-atom pairwise interactions via physics-informed equations parameterized with neural networks.
We further improved the model generalization by augmenting a range of binding poses and to broader training data.
arXiv Detail & Related papers (2020-08-22T14:29:58Z) - Model Fusion with Kullback--Leibler Divergence [58.20269014662046]
We propose a method to fuse posterior distributions learned from heterogeneous datasets.
Our algorithm relies on a mean field assumption for both the fused model and the individual dataset posteriors.
arXiv Detail & Related papers (2020-07-13T03:27:45Z)
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.