Chemical complexity challenge: Is multi-instance machine learning a solution?

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15110%2F24%3A73629898" target="_blank" >RIV/61989592:15110/24:73629898 - isvavai.cz</a>

Result on the web

<a href="https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.1698" target="_blank" >https://wires.onlinelibrary.wiley.com/doi/epdf/10.1002/wcms.1698</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1002/wcms.1698" target="_blank" >10.1002/wcms.1698</a>

Result language

angličtina

Original language name

Chemical complexity challenge: Is multi-instance machine learning a solution?

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30107 - Medicinal chemistry

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Wiley Interdisciplinary Reviews-Computational Molecular Science

ISSN

1759-0876

e-ISSN

1759-0884

Volume of the periodical

14

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

27

Pages from-to

"e1698"

UT code for WoS article

001120010200001

EID of the result in the Scopus database

2-s2.0-85177824629