Molecular representations in bio-cheminformatics
Thanh‐Hoang Nguyen‐Vo, Paul Teesdale‐Spittle, Joanne E. Harvey, Binh P. Nguyen
Abstract
Abstract Molecular representations have essential roles in bio-cheminformatics as they facilitate the growth of machine learning applications in numerous sub-domains of biology and chemistry, especially drug discovery. These representations transform the structural and chemical information of molecules into machine-readable formats that can be efficiently processed by computer programs. In this paper, we present a comprehensive review, providing readers with diverse perspectives on the strengths and weaknesses of well-known molecular representations, along with their respective categories and implementation sources. Moreover, we provide a summary of the applicability of these representations in de novo molecular design, molecular property prediction, and chemical reactions. Besides, representations for macromolecules are discussed with highlighted pros and cons. By addressing these aspects, we aim to offer a valuable resource on the significant role of molecular representations in advancing bio-cheminformatics and its related domains.