mlsanityData to 7 Oct 2026

Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches

arXiv 2408.09461 ↗First posted 18 Aug 2024cs.LG cond-mat.mtrl-sci physics.chem-phcited by 2

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Abstract (arXiv)

Molecular Property Prediction (MPP) plays a pivotal role across diverse domains, spanning drug discovery, material science, and environmental chemistry. Fueled by the exponential growth of chemical data and the evolution of artificial intelligence, recent years have witnessed remarkable strides in MPP. However, the multifaceted nature of molecular data, such as molecular structures, SMILES notation, and molecular images, continues to pose a fundamental challenge in its effective representation. To address this, representation learning techniques are instrumental as they acquire informative and interpretable representations of molecular data. This article explores recent AI/-based approaches in MPP, focusing on both single and multiple modality representation techniques. It provides an overview of various molecule representations and encoding schemes, categorizes MPP methods by their use of modalities, and outlines datasets and tools available for feature generation. The article also analyzes the performance of recent methods and suggests future research directions to advance the field of MPP.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 48 of its 184 references that are in the map. First posted on arXiv 18 Aug 2024.

Builds on

48 of 184 references.

References · 48

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Augmenting Molecular Graphs with Geometries via Machine Learning Interatomic Potentialscited by 1
  2. 2026A Systematic Survey and Benchmark of Deep Learning for Molecular Property Prediction in the Foundation Model Eracited by 1