2024/08/18 by Tanya Liyaqat, Liyaqat, Tanya, Tanvir Ahmad +3 · 1 citation
Chemistry · Computer Science · Materials Science · #Biomolecules (q-bio.BM) #Chemical Physics (physics.chem-ph) #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Various Chemistry Research Topics
paper · pdf · doi:10.48550/arxiv.2408.09461
openalex publication_date 2024/08/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.