Message Passing Networks for Molecules with Tetrahedral Chirality

• URL: http://arxiv.org/abs/2012.00094v2
• Date: Fri, 4 Dec 2020 15:43:10 GMT
• Title: Message Passing Networks for Molecules with Tetrahedral Chirality
• Authors: Lagnajit Pattanaik, Octavian-Eugen Ganea, Ian Coley, Klavs F. Jensen, William H. Green, Connor W. Coley
• Abstract summary: We develop two custom aggregation functions for message passing neural networks to learn properties of molecules with tetrahedral chirality. Results show modest improvements over a baseline sum aggregator, highlighting opportunities for further architecture development.
• Score: 8.391459650489123
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Molecules with identical graph connectivity can exhibit different physical and biological properties if they exhibit stereochemistry-a spatial structural characteristic. However, modern neural architectures designed for learning structure-property relationships from molecular structures treat molecules as graph-structured data and therefore are invariant to stereochemistry. Here, we develop two custom aggregation functions for message passing neural networks to learn properties of molecules with tetrahedral chirality, one common form of stereochemistry. We evaluate performance on synthetic data as well as a newly-proposed protein-ligand docking dataset with relevance to drug discovery. Results show modest improvements over a baseline sum aggregator, highlighting opportunities for further architecture development.

Related papers

• Knowledge-aware contrastive heterogeneous molecular graph learning [77.94721384862699]
  We propose a paradigm shift by encoding molecular graphs into Heterogeneous Molecular Graph Learning (KCHML) KCHML conceptualizes molecules through three distinct graph views-molecular, elemental, and pharmacological-enhanced by heterogeneous molecular graphs and a dual message-passing mechanism. This design offers a comprehensive representation for property prediction, as well as for downstream tasks such as drug-drug interaction (DDI) prediction.
  arXiv  Detail & Related papers  (2025-02-17T11:53:58Z)
• MOL-Mamba: Enhancing Molecular Representation with Structural & Electronic Insights [23.55889965960128]
  We introduce MOL-Mamba, a framework that enhances molecular representation by combining structural and electronic insights. MOL-Mamba outperforms state-of-the-art baselines across eleven chemical-biological molecular datasets.
  arXiv  Detail & Related papers  (2024-12-21T04:48:57Z)
• 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)
• FragNet: A Graph Neural Network for Molecular Property Prediction with Four Layers of Interpretability [0.7499722271664147]
  We introduce the FragNet architecture, a graph neural network capable of achieving prediction accuracies comparable to the current state-of-the-art models. This model enables understanding of which atoms, covalent bonds, molecular fragments, and molecular fragment connections are critical in the prediction of a given molecular property. The interpretable capabilities of FragNet are key to gaining scientific insights from the model's learned patterns between molecular structure and molecular properties.
  arXiv  Detail & Related papers  (2024-10-16T01:37:01Z)
• SE3Set: Harnessing equivariant hypergraph neural networks for molecular representation learning [27.713870291922333]
  We develop an SE(3) equivariant hypergraph neural network architecture tailored for advanced molecular representation learning. SE3Set has shown performance on par with state-of-the-art (SOTA) models for small molecule datasets. It excels on the MD22 dataset, achieving a notable improvement of approximately 20% in accuracy across all molecules.
  arXiv  Detail & Related papers  (2024-05-26T10:43:16Z)
• Multi-channel learning for integrating structural hierarchies into context-dependent molecular representation [10.025809630976065]
  This paper introduces a novel pre-training framework that learns robust and generalizable chemical knowledge. Our approach demonstrates competitive performance across various molecular property benchmarks.
  arXiv  Detail & Related papers  (2023-11-05T23:47:52Z)
• Bi-level Contrastive Learning for Knowledge-Enhanced Molecule Representations [68.32093648671496]
  We introduce GODE, which accounts for the dual-level structure inherent in molecules. Molecules possess an intrinsic graph structure and simultaneously function as nodes within a broader molecular knowledge graph. By pre-training two GNNs on different graph structures, GODE effectively fuses molecular structures with their corresponding knowledge graph substructures.
  arXiv  Detail & Related papers  (2023-06-02T15:49:45Z)
• Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning [57.670845619155195]
  We introduce a new model for molecular representation learning called the Atomic and Subgraph-aware Bilateral Aggregation (ASBA) ASBA addresses the limitations of previous atom-wise and subgraph-wise models by incorporating both types of information. Our method offers a more comprehensive way to learn representations for molecular property prediction and has broad potential in drug and material discovery applications.
  arXiv  Detail & Related papers  (2023-05-22T00:56:00Z)
• An Equivariant Generative Framework for Molecular Graph-Structure Co-Design [54.92529253182004]
  We present MolCode, a machine learning-based generative framework for underlineMolecular graph-structure underlineCo-design. In MolCode, 3D geometric information empowers the molecular 2D graph generation, which in turn helps guide the prediction of molecular 3D structure. Our investigation reveals that the 2D topology and 3D geometry contain intrinsically complementary information in molecule design.
  arXiv  Detail & Related papers  (2023-04-12T13:34:22Z)
• Implicit Geometry and Interaction Embeddings Improve Few-Shot Molecular Property Prediction [53.06671763877109]
  We develop molecular embeddings that encode complex molecular characteristics to improve the performance of few-shot molecular property prediction. Our approach leverages large amounts of synthetic data, namely the results of molecular docking calculations. On multiple molecular property prediction benchmarks, training from the embedding space substantially improves Multi-Task, MAML, and Prototypical Network few-shot learning performance.
  arXiv  Detail & Related papers  (2023-02-04T01:32:40Z)
• Flexible dual-branched message passing neural network for quantum mechanical property prediction with molecular conformation [16.08677447593939]
  We propose a dual-branched neural network for molecular property prediction based on message-passing framework. Our model learns heterogeneous molecular features with different scales, which are trained flexibly according to each prediction target.
  arXiv  Detail & Related papers  (2021-06-14T10:00:39Z)

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.