Industry-scale application and evaluation of deep learning for drug target prediction

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27740%2F20%3A10244864" target="_blank" >RIV/61989100:27740/20:10244864 - isvavai.cz</a>

Result on the web

<a href="https://jcheminf.biomedcentral.com/track/pdf/10.1186/s13321-020-00428-5" target="_blank" >https://jcheminf.biomedcentral.com/track/pdf/10.1186/s13321-020-00428-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s13321-020-00428-5" target="_blank" >10.1186/s13321-020-00428-5</a>

Result language

angličtina

Original language name

Industry-scale application and evaluation of deep learning for drug target prediction

Original language description

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.

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

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/LM2018140" target="_blank" >LM2018140: e-Infrastructure CZ</a><br>

Continuities

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

Publication year

2020

Confidentiality

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

Name of the periodical

Journal of Cheminformatics

ISSN

1758-2946

e-ISSN

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Volume of the periodical

12

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

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UT code for WoS article

000529286600001

EID of the result in the Scopus database

2-s2.0-85084299133