Implicit Neural Representations of Molecular Vector-Valued Functions
This provides a novel representation for molecules that could enhance tasks like drug discovery by capturing features not addressed by existing methods, though it is incremental as it builds on implicit neural representations.
The authors introduced molecular neural fields as a new representation of molecules using neural networks to parameterize vector-valued functions, enabling tasks like shape-based molecule generation and resolution-independent interpolation between conformations, with proofs-of-concept including superresolution reconstruction of a protein-ligand complex and latent space embedding of molecular volumes.
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.