Related papers: SPIN: SE(3)-Invariant Physics Informed Network for Binding Affinity Prediction

SPIN: SE(3)-Invariant Physics Informed Network for Binding Affinity Prediction

URL: http://arxiv.org/abs/2407.11057v1
Date: Wed, 10 Jul 2024 08:40:07 GMT
Title: SPIN: SE(3)-Invariant Physics Informed Network for Binding Affinity Prediction
Authors: Seungyeon Choi, Sangmin Seo, Sanghyun Park,
Abstract summary: 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.
Score: 3.406882192023597
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Accurate prediction of protein-ligand binding affinity is crucial for rapid and efficient drug development. Recently, the importance of predicting binding affinity has led to increased attention on research that models the three-dimensional structure of protein-ligand complexes using graph neural networks to predict binding affinity. However, traditional methods often fail to accurately model the complex's spatial information or rely solely on geometric features, neglecting the principles of protein-ligand binding. This can lead to overfitting, resulting in models that perform poorly on independent datasets and ultimately reducing their usefulness in real drug development. To address this issue, we propose SPIN, a model designed to achieve superior generalization by incorporating various inductive biases applicable to this task, beyond merely training on empirical data from datasets. For prediction, we defined two types of inductive biases: a geometric perspective that maintains consistent binding affinity predictions regardless of the complexs rotations and translations, and a physicochemical perspective that necessitates minimal binding free energy along their reaction coordinate for effective protein-ligand binding. These prior knowledge inputs enable the SPIN to outperform comparative models in benchmark sets such as CASF-2016 and CSAR HiQ. Furthermore, we demonstrated the practicality of our model through virtual screening experiments and validated the reliability and potential of our proposed model based on experiments assessing its interpretability.

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