Implicit Neural Representations of Molecular Vector-Valued Functions

• URL: http://arxiv.org/abs/2502.10848v1
• Date: Sat, 15 Feb 2025 16:37:56 GMT
• Title: Implicit Neural Representations of Molecular Vector-Valued Functions
• Authors: Jirka Lhotka, Daniel Probst,
• Abstract summary: We introduce the representation of molecules through vector-valued functions, or $n$-dimensional vector fields, that are parameterized by neural networks. Unlike surface representations, molecular neural fields capture external features and the hydrophobic core of macromolecules such as proteins. These properties inherited by molecular neural fields lend themselves to tasks including the generation of molecules based on their desired shape, structure, and composition.
• Score: 1.1409922116510922
• License:
• Abstract: Molecules have various computational representations, including numerical descriptors, strings, graphs, point clouds, and surfaces. Each representation method enables the application of various machine learning methodologies from linear regression to graph neural networks paired with large language models. To complement existing representations, we introduce the representation of molecules through vector-valued functions, or $n$-dimensional vector fields, that are parameterized by neural networks, which we denote molecular neural fields. Unlike surface representations, molecular neural fields capture external features and the hydrophobic core of macromolecules such as proteins. Compared to discrete graph or point representations, molecular neural fields are compact, resolution independent and inherently suited for interpolation in spatial and temporal dimensions. These properties inherited by molecular neural fields lend themselves to tasks including the generation of molecules based on their desired shape, structure, and composition, and the resolution-independent interpolation between molecular conformations in space and time. Here, we provide a framework and proofs-of-concept for molecular neural fields, namely, the parametrization and superresolution reconstruction of a protein-ligand complex using an auto-decoder architecture and the embedding of molecular volumes in latent space using an auto-encoder architecture.

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)
• 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)
• Molecular Graph Representation Learning via Structural Similarity Information [11.38130169319915]
  We introduce the textbf Structural Similarity Motif GNN (MSSM-GNN), a novel molecular graph representation learning method. In particular, we propose a specially designed graph that leverages graph kernel algorithms to represent the similarity between molecules quantitatively. We employ GNNs to learn feature representations from molecular graphs, aiming to enhance the accuracy of property prediction by incorporating additional molecular representation information.
  arXiv  Detail & Related papers  (2024-09-13T06:59:10Z)
• CTAGE: Curvature-Based Topology-Aware Graph Embedding for Learning Molecular Representations [11.12640831521393]
  We propose an embedding approach CTAGE, utilizing $k$-hop discrete Ricci curvature to extract structural insights from molecular graph data. Results indicate that introducing node curvature significantly improves the performance of current graph neural network frameworks.
  arXiv  Detail & Related papers  (2023-07-25T06:13:01Z)
• 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)
• MUDiff: Unified Diffusion for Complete Molecule Generation [104.7021929437504]
  We present a new model for generating a comprehensive representation of molecules, including atom features, 2D discrete molecule structures, and 3D continuous molecule coordinates. We propose a novel graph transformer architecture to denoise the diffusion process. Our model is a promising approach for designing stable and diverse molecules and can be applied to a wide range of tasks in molecular modeling.
  arXiv  Detail & Related papers  (2023-04-28T04:25:57Z)
• Learning Harmonic Molecular Representations on Riemannian Manifold [18.49126496517951]
  Molecular representation learning plays a crucial role in AI-assisted drug discovery research. We propose a Harmonic Molecular Representation learning framework, which represents a molecule using the Laplace-Beltrami eigenfunctions of its molecular surface.
  arXiv  Detail & Related papers  (2023-03-27T18:02:47Z)
• 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)
• Molecular dynamics without molecules: searching the conformational space of proteins with generative neural networks [0.0]
  All-atom and coarse-grained molecular dynamics are widely used to study the conformational states of proteins. All-atom and coarse-grained simulation methods suffer from the fact that without access to supercomputing resources, the time and length scales at which these states become detectable are difficult to achieve. One alternative is based on encoding the atomistic trajectory of molecular dynamics as a shorthand version of physical particles, and then learning to propagate the encoded trajectory through the use of artificial intelligence vectors.
  arXiv  Detail & Related papers  (2022-06-09T02:06:43Z)
• Geometric Transformer for End-to-End Molecule Properties Prediction [92.28929858529679]
  We introduce a Transformer-based architecture for molecule property prediction, which is able to capture the geometry of the molecule. We modify the classical positional encoder by an initial encoding of the molecule geometry, as well as a learned gated self-attention mechanism.
  arXiv  Detail & Related papers  (2021-10-26T14:14:40Z)
• Molecular CT: Unifying Geometry and Representation Learning for Molecules at Different Scales [3.987395340580183]
  A new deep neural network architecture, Molecular Configuration Transformer ( Molecular CT), is introduced for this purpose. The computational efficiency and universality make Molecular CT versatile for a variety of molecular learning scenarios. As examples, we show that Molecular CT enables representational learning for molecular systems at different scales, and achieves comparable or improved results on common benchmarks.
  arXiv  Detail & Related papers  (2020-12-22T03:41:16Z)

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.