Enhancing Molecular Design through Graph-based Topological Reinforcement Learning

• URL: http://arxiv.org/abs/2411.14726v1
• Date: Fri, 22 Nov 2024 04:45:55 GMT
• Title: Enhancing Molecular Design through Graph-based Topological Reinforcement Learning
• Authors: Xiangyu Zhang,
• Abstract summary: We present Graph-based Topological Reinforcement Learning (GraphTRL), which integrates both chemical and structural data for improved molecular generation. Evaluations show that GraphTRL outperforms existing methods in binding affinity prediction, offering a promising approach to accelerate drug discovery.
• Score: 10.632524607651105
• License:
• Abstract: The generation of drug-like molecules is crucial for drug design. Existing reinforcement learning (RL) methods often overlook structural information. However, feature engineering-based methods usually merely focus on binding affinity prediction without substantial molecular modification. To address this, we present Graph-based Topological Reinforcement Learning (GraphTRL), which integrates both chemical and structural data for improved molecular generation. GraphTRL leverages multiscale weighted colored graphs (MWCG) and persistent homology, combined with molecular fingerprints, as the state space for RL. Evaluations show that GraphTRL outperforms existing methods in binding affinity prediction, offering a promising approach to accelerate drug discovery.

Related papers

• GraphXForm: Graph transformer for computer-aided molecular design with application to extraction [73.1842164721868]
  We present GraphXForm, a decoder-only graph transformer architecture, which is pretrained on existing compounds and then fine-tuned. We evaluate it on two solvent design tasks for liquid-liquid extraction, showing that it outperforms four state-of-the-art molecular design techniques.
  arXiv  Detail & Related papers  (2024-11-03T19:45:15Z)
• FARM: Functional Group-Aware Representations for Small Molecules [55.281754551202326]
  We introduce Functional Group-Aware Representations for Small Molecules (FARM) FARM is a foundation model designed to bridge the gap between SMILES, natural language, and molecular graphs. We rigorously evaluate FARM on the MoleculeNet dataset, where it achieves state-of-the-art performance on 10 out of 12 tasks.
  arXiv  Detail & Related papers  (2024-10-02T23:04:58Z)
• Contrastive Dual-Interaction Graph Neural Network for Molecular Property Prediction [0.0]
  We introduce DIG-Mol, a novel self-supervised graph neural network framework for molecular property prediction. DIG-Mol integrates a momentum distillation network with two interconnected networks to efficiently improve molecular characterization. We have established DIG-Mol's state-of-the-art performance through extensive experimental evaluation in a variety of molecular property prediction tasks.
  arXiv  Detail & Related papers  (2024-05-04T10:09:27Z)
• Molecular Property Prediction Based on Graph Structure Learning [29.516479802217205]
  We propose a graph structure learning (GSL) based MPP approach, called GSL-MPP. Specifically, we first apply graph neural network (GNN) over molecular graphs to extract molecular representations. With molecular fingerprints, we construct a molecular similarity graph (MSG)
  arXiv  Detail & Related papers  (2023-12-28T06:45:13Z)
• GraphCL-DTA: a graph contrastive learning with molecular semantics for drug-target binding affinity prediction [2.523552067304274]
  GraphCL-DTA is a graph contrastive learning framework for molecular graphs to learn drug representations. Next, we design a new loss function that can be directly used to adjust the uniformity of drug and target representations. The effectiveness of the above innovative elements is verified on two real datasets.
  arXiv  Detail & Related papers  (2023-07-18T06:01:37Z)
• Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations [55.42602325017405]
  We propose a novel method called GODE, which takes into account the two-level structure of individual molecules. By pre-training two graph neural networks (GNNs) on different graph structures, combined with contrastive learning, GODE fuses molecular structures with their corresponding knowledge graph substructures. When fine-tuned across 11 chemical property tasks, our model outperforms existing benchmarks, registering an average ROC-AUC uplift of 13.8% for classification tasks and an average RMSE/MAE enhancement of 35.1% for regression tasks.
  arXiv  Detail & Related papers  (2023-06-02T15:49:45Z)
• MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning [77.31492888819935]
  We propose a novel paradigm of "pre-train, prompt, fine-tune" for molecular representation learning, named molecule continuous prompt tuning (MolCPT) MolCPT defines a motif prompting function that uses the pre-trained model to project the standalone input into an expressive prompt. Experiments on several benchmark datasets show that MolCPT efficiently generalizes pre-trained GNNs for molecular property prediction.
  arXiv  Detail & Related papers  (2022-12-20T19:32:30Z)
• Graph neural networks for the prediction of molecular structure-property relationships [59.11160990637615]
  Graph neural networks (GNNs) are a novel machine learning method that directly work on the molecular graph. GNNs allow to learn properties in an end-to-end fashion, thereby avoiding the need for informative descriptors. We describe the fundamentals of GNNs and demonstrate the application of GNNs via two examples for molecular property prediction.
  arXiv  Detail & Related papers  (2022-07-25T11:30:44Z)
• Graph-based Molecular Representation Learning [59.06193431883431]
  Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. Recently, MRL has achieved considerable progress, especially in methods based on deep molecular graph learning.
  arXiv  Detail & Related papers  (2022-07-08T17:43:20Z)
• KPGT: Knowledge-Guided Pre-training of Graph Transformer for Molecular Property Prediction [13.55018269009361]
  We introduce Knowledge-guided Pre-training of Graph Transformer (KPGT), a novel self-supervised learning framework for molecular graph representation learning. KPGT can offer superior performance over current state-of-the-art methods on several molecular property prediction tasks.
  arXiv  Detail & Related papers  (2022-06-02T08:22:14Z)
• Molecular Graph Generation via Geometric Scattering [7.796917261490019]
  Graph neural networks (GNNs) have been used extensively for addressing problems in drug design and discovery. We propose a representation-first approach to molecular graph generation. We show that our architecture learns meaningful representations of drug datasets and provides a platform for goal-directed drug synthesis.
  arXiv  Detail & Related papers  (2021-10-12T18:00:23Z)

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.