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

• URL: http://arxiv.org/abs/2408.09461v2
• Date: Thu, 22 Aug 2024 07:59:30 GMT
• Title: Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches
• Authors: Tanya Liyaqat, Tanvir Ahmad, Chandni Saxena,
• Abstract summary: Molecular Property Prediction (MPP) plays a pivotal role across diverse domains, spanning drug discovery, material science, and environmental chemistry. Recent years have witnessed remarkable strides in MPP, fueled by the exponential growth of chemical data and the evolution of artificial intelligence. This article explores recent AI/based approaches in MPP, focusing on both single and multiple modality representation techniques.
• Score: 1.0446041735532203
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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