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- Title
Chemical complexity challenge: Is multi‐instance machine learning a solution?
- Authors
Zankov, Dmitry; Madzhidov, Timur; Varnek, Alexandre; Polishchuk, Pavel
- Abstract
Molecules are complex dynamic objects that can exist in different molecular forms (conformations, tautomers, stereoisomers, protonation states, etc.) and often it is not known which molecular form is responsible for observed physicochemical and biological properties of a given molecule. This raises the problem of the selection of the correct molecular form for machine learning modeling of target properties. The same problem is common to biological molecules (RNA, DNA, proteins)—long sequences where only key segments, which often cannot be located precisely, are involved in biological functions. Multi‐instance machine learning (MIL) is an efficient approach for solving problems where objects under study cannot be uniquely represented by a single instance, but rather by a set of multiple alternative instances. Multi‐instance learning was formalized in 1997 and motivated by the problem of conformation selection in drug activity prediction tasks. Since then MIL has found a lot of applications in various domains, such as information retrieval, computer vision, signal processing, bankruptcy prediction, and so on. In the given review we describe the MIL framework and its applications to the tasks associated with ambiguity in the representation of small and biological molecules in chemoinformatics and bioinformatics. We have collected examples that demonstrate the advantages of MIL over the traditional single‐instance learning (SIL) approach. Special attention was paid to the ability of MIL models to identify key instances responsible for a modeling property. This article is categorized under:Data Science > ChemoinformaticsData Science > Artificial Intelligence/Machine Learning
- Subjects
MACHINE learning; BIOMOLECULES; ARTIFICIAL intelligence; COMPUTER vision; SIGNAL processing; MEDICAL informatics; DNA data banks
- Publication
WIREs: Computational Molecular Science, 2024, Vol 14, Issue 1, p1
- ISSN
1759-0876
- Publication type
Article
- DOI
10.1002/wcms.1698