EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
- URL: http://arxiv.org/abs/2202.05146v1
- Date: Mon, 7 Feb 2022 16:26:05 GMT
- Title: EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction
- Authors: Hannes St\"ark, Octavian-Eugen Ganea, Lagnajit Pattanaik, Regina
Barzilay, Tommi Jaakkola
- Abstract summary: Predicting how a drug-like molecule binds to a specific protein target is a core problem in drug discovery.
An extremely fast computational binding method would enable key applications such as fast virtual screening or drug engineering.
We present EquiBind, an SE(3)-equivariant geometric deep learning model performing direct-shot prediction of both i) the receptor binding location (blind docking) and ii) the ligand's bound pose and orientation.
- Score: 31.191844909335963
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Predicting how a drug-like molecule binds to a specific protein target is a
core problem in drug discovery. An extremely fast computational binding method
would enable key applications such as fast virtual screening or drug
engineering. Existing methods are computationally expensive as they rely on
heavy candidate sampling coupled with scoring, ranking, and fine-tuning steps.
We challenge this paradigm with EquiBind, an SE(3)-equivariant geometric deep
learning model performing direct-shot prediction of both i) the receptor
binding location (blind docking) and ii) the ligand's bound pose and
orientation. EquiBind achieves significant speed-ups and better quality
compared to traditional and recent baselines. Further, we show extra
improvements when coupling it with existing fine-tuning techniques at the cost
of increased running time. Finally, we propose a novel and fast fine-tuning
model that adjusts torsion angles of a ligand's rotatable bonds based on
closed-form global minima of the von Mises angular distance to a given input
atomic point cloud, avoiding previous expensive differential evolution
strategies for energy minimization.
Related papers
- Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion
Bridge [69.80471117520719]
Re-Dock is a novel diffusion bridge generative model extended to geometric manifold.
We propose energy-to-geometry mapping inspired by the Newton-Euler equation to co-model the binding energy and conformations.
Experiments on designed benchmark datasets including apo-dock and cross-dock demonstrate our model's superior effectiveness and efficiency over current methods.
arXiv Detail & Related papers (2024-02-18T05:04:50Z) - ETDock: A Novel Equivariant Transformer for Protein-Ligand Docking [36.14826783009814]
Traditional docking methods rely on scoring functions and deep learning to predict the docking between proteins and drugs.
In this paper, we propose a transformer neural network for protein-ligand docking pose prediction.
The experimental results on real datasets show that our model can achieve state-of-the-art performance.
arXiv Detail & Related papers (2023-10-12T06:23:12Z) - 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) - Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's
Rotation Equation [18.70508112639968]
Protein-ligand binding prediction is a fundamental problem in AI-driven drug discovery.
In this paper, we explore unsupervised approaches and reformulate binding energy prediction as a generative modeling task.
We train an energy-based model on a set of unlabelled protein-ligand complexes using SE(3) denoising score matching and interpret its log-likelihood as binding affinity.
arXiv Detail & Related papers (2023-01-25T20:33:51Z) - STORM+: Fully Adaptive SGD with Momentum for Nonconvex Optimization [74.1615979057429]
We investigate non-batch optimization problems where the objective is an expectation over smooth loss functions.
Our work builds on the STORM algorithm, in conjunction with a novel approach to adaptively set the learning rate and momentum parameters.
arXiv Detail & Related papers (2021-11-01T15:43:36Z) - Error-Correcting Neural Networks for Semi-Lagrangian Advection in the
Level-Set Method [0.0]
We present a machine learning framework that blends image super-resolution technologies with scalar transport in the level-set method.
We investigate whether we can compute on-the-fly data-driven corrections to minimize numerical viscosity in the coarse-mesh evolution of an interface.
arXiv Detail & Related papers (2021-10-22T06:36:15Z) - Acceleration Methods [57.202881673406324]
We first use quadratic optimization problems to introduce two key families of acceleration methods.
We discuss momentum methods in detail, starting with the seminal work of Nesterov.
We conclude by discussing restart schemes, a set of simple techniques for reaching nearly optimal convergence rates.
arXiv Detail & Related papers (2021-01-23T17:58:25Z) - Deep Magnification-Flexible Upsampling over 3D Point Clouds [103.09504572409449]
We propose a novel end-to-end learning-based framework to generate dense point clouds.
We first formulate the problem explicitly, which boils down to determining the weights and high-order approximation errors.
Then, we design a lightweight neural network to adaptively learn unified and sorted weights as well as the high-order refinements.
arXiv Detail & Related papers (2020-11-25T14:00:18Z) - Learning to Optimize Non-Rigid Tracking [54.94145312763044]
We employ learnable optimizations to improve robustness and speed up solver convergence.
First, we upgrade the tracking objective by integrating an alignment data term on deep features which are learned end-to-end through CNN.
Second, we bridge the gap between the preconditioning technique and learning method by introducing a ConditionNet which is trained to generate a preconditioner.
arXiv Detail & Related papers (2020-03-27T04:40:57Z)
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.