Protein Structure Prediction in the 3D HP Model Using Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2412.20329v1
• Date: Sun, 29 Dec 2024 02:55:54 GMT
• Title: Protein Structure Prediction in the 3D HP Model Using Deep Reinforcement Learning
• Authors: Giovanny Espitia, Yui Tik Pang, James C. Gumbart,
• Abstract summary: We address protein structure prediction in the 3D Hydrophobic-Polar lattice model through two novel deep learning architectures.<n>For proteins under 36 residues, our hybrid reservoir-based model combines fixed random projections with trainable deep layers, achieving optimal conformations with 25% fewer training episodes.<n>For longer sequences, we employ a long short-term memory network with multi-headed attention, matching best-known energy values.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We address protein structure prediction in the 3D Hydrophobic-Polar lattice model through two novel deep learning architectures. For proteins under 36 residues, our hybrid reservoir-based model combines fixed random projections with trainable deep layers, achieving optimal conformations with 25% fewer training episodes. For longer sequences, we employ a long short-term memory network with multi-headed attention, matching best-known energy values. Both architectures leverage a stabilized Deep Q-Learning framework with experience replay and target networks, demonstrating consistent achievement of optimal conformations while significantly improving training efficiency compared to existing methods.

Related papers

• Enhancing Reinforcement learning in 3-Dimensional Hydrophobic-Polar Protein Folding Model with Attention-based layers [0.0]
  We adapt a Deep Q-Network (DQN) integrated with attention mechanisms (Transformers) to address the 3D H-P protein folding problem. Our system formulates folding decisions as a self-avoiding walk in a reinforced environment, and employs a specialized reward function based on favorable hydrophobic interactions.
  arXiv  Detail & Related papers  (2025-04-22T06:53:36Z)
• Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning [54.584665518334035]
  Hybrid architectures that combine Attention and State Space Models (SSMs) achieve state-of-the-art accuracy and runtime performance. Recent work has demonstrated that applying compression and distillation to Attention-only models yields smaller, more accurate models at a fraction of the training cost. We introduce a novel group-aware pruning strategy that preserves the structural integrity of SSM blocks and their sequence modeling capabilities.
  arXiv  Detail & Related papers  (2025-04-15T17:26:29Z)
• CDGS: Confidence-Aware Depth Regularization for 3D Gaussian Splatting [5.8678184183132265]
  CDGS is a confidence-aware depth regularization approach developed to enhance 3DGS. We leverage multi-cue confidence maps of monocular depth estimation and sparse Structure-from-Motion depth to adaptively adjust depth supervision. Our method demonstrates improved geometric detail preservation in early training stages and achieves competitive performance in both NVS quality and geometric accuracy.
  arXiv  Detail & Related papers  (2025-02-20T16:12:13Z)
• On Machine Learning Approaches for Protein-Ligand Binding Affinity Prediction [2.874893537471256]
  This study evaluates the performance of classical tree-based models and advanced neural networks in protein-ligand binding affinity prediction. We show that combining 2D and 3D model strengths improves active learning outcomes beyond current state-of-the-art approaches.
  arXiv  Detail & Related papers  (2024-07-15T13:06:00Z)
• xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein [74.64101864289572]
  We propose a unified protein language model, xTrimoPGLM, to address protein understanding and generation tasks simultaneously.<n>xTrimoPGLM significantly outperforms other advanced baselines in 18 protein understanding benchmarks across four categories.<n>It can also generate de novo protein sequences following the principles of natural ones, and can perform programmable generation after supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-01-11T15:03:17Z)
• Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks [68.90692290665648]
  We integrate knowledge learned by protein language models into several state-of-the-art geometric networks. Our findings show an overall improvement of 20% over baselines. Strong evidence indicates that the incorporation of protein language models' knowledge enhances geometric networks' capacity by a significant margin.
  arXiv  Detail & Related papers  (2022-12-07T04:04:04Z)
• AlphaFold Distillation for Protein Design [25.190210443632825]
  Inverse protein folding is crucial in bio-engineering and drug discovery. Forward folding models like AlphaFold offer a potential solution by accurately predicting structures from sequences. We propose using knowledge distillation on folding model confidence metrics to create a faster and end-to-end differentiable distilled model.
  arXiv  Detail & Related papers  (2022-10-05T19:43:06Z)
• EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models [53.17320541056843]
  We propose a fully-differentiable approach for protein structure optimization, guided by a data-driven generative network. Our EBM-Fold approach can efficiently produce high-quality decoys, compared against traditional Rosetta-based structure optimization routines.
  arXiv  Detail & Related papers  (2021-05-11T03:40:29Z)
• Multi-view Depth Estimation using Epipolar Spatio-Temporal Networks [87.50632573601283]
  We present a novel method for multi-view depth estimation from a single video. Our method achieves temporally coherent depth estimation results by using a novel Epipolar Spatio-Temporal (EST) transformer. To reduce the computational cost, inspired by recent Mixture-of-Experts models, we design a compact hybrid network.
  arXiv  Detail & Related papers  (2020-11-26T04:04:21Z)
• DeepFoldit -- A Deep Reinforcement Learning Neural Network Folding Proteins [0.0]
  We trained a deep reinforcement neural network called DeepFoldit to improve the score assigned to an unfolded protein. Our approach combines the intuitive user interface of Foldit with the efficiency of deep reinforcement learning.
  arXiv  Detail & Related papers  (2020-10-28T16:05:42Z)
• Growing Efficient Deep Networks by Structured Continuous Sparsification [34.7523496790944]
  We develop an approach to growing deep network architectures over the course of training. Our method can start from a small, simple seed architecture and dynamically grow and prune both layers and filters. We achieve $49.7%$ inference FLOPs and $47.4%$ training FLOPs savings compared to a baseline ResNet-50 on ImageNet.
  arXiv  Detail & Related papers  (2020-07-30T10:03:47Z)
• FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining [65.39532971991778]
  We present an accuracy predictor that scores architecture and training recipes jointly, guiding both sample selection and ranking. We run fast evolutionary searches in just CPU minutes to generate architecture-recipe pairs for a variety of resource constraints. FBNetV3 makes up a family of state-of-the-art compact neural networks that outperform both automatically and manually-designed competitors.
  arXiv  Detail & Related papers  (2020-06-03T05:20:21Z)

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.