Naslov
Possibility of discrimination between approved and withdrawn drugs using multivariate classification structure-property models based on molecular descriptors

Autori
Lučić, Bono ; Stepanić, Višnja ; Fa, Dionizije ; Jović, Ozren ; Lipić, Tomislav ; Šmuc, Tomislav

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, sažetak, znanstveni

Izvornik
Math/Chem/Comp 2022 and 33rd MC2 Conference : Book of Abstracts / Vančik, Hrvoj ; Cioslowski, Jerzy ; Namjesnik, Danijel - Zagreb : Hrvatsko kemijsko društvo, 2022, 21-21

ISBN
978-953-8334-03-0

Skup
33rd MC2 Conference (Math/Chem/Comp 2022)

Mjesto i datum
Dubrovnik, Hrvatska, 06.06.2022. - 10.06.2022

Vrsta sudjelovanja
Predavanje

Vrsta recenzije
Recenziran

Ključne riječi
drug ; withdrawn drug ; QSAR ; AI model ; classification

Sažetak
There is enormous public interest in the EU, but also the interest of the pharmaceutical industry in the possibility of finding significant factors that would make it possible to distinguish between approved drugs and drugs that have been withdrawn after several years of use. The EU responded to this interest by funding a large AI4EU project, which launched calls for proposals to develop various artificial intelligence (AI)-based computing solutions.[1] Among them was the task to develop a predictive AI solution based on the deep neural network method to distinguish drugs from withdrawn drugs based on their structure.[2] In this presentation, the process of defining the problem and creating a dataset will be elaborated, as well as related issues such as the different status of the same drug in different countries or regions, or the problem of cleaning datasets, as many drugs are not pure compounds but complexes. We have calculated comprehensive sets of molecular structure descriptors for the final sets of approved and withdrawn drugs. The analysis of the importance/usefulness of each structure-based molecular descriptor (including those related to ADMETox properties of drugs) in discriminating between approved and withdrawn drugs will be presented. As expected, the classification accuracy of the multivariate models based on descriptors is not high. Nevertheless, it is comparable to (or sometimes better than) more complex models and significantly higher than the accuracy achieved by random guessing (random model). REFERENCES [1] The AI4EU Call for Solutions Seeking Companies to Solve AI Challenges, https://internationaldataspaces.org/the-ai4eu-call-for-solutions-seeking-companies-to-solve-aichallenges/ (assessed on April 15, 2022) [2] The AI-on-demand platform, https://www.ai4europe.eu/ and https://www.ai4media.eu/projectoverview/ (assessed on April 15, 2022)

Izvorni jezik
Engleski

Znanstvena područja
Kemija, Računarstvo, Interdisciplinarne tehničke znanosti

Napomena
Drug Attrition Oracle (AI4EU supported project), HORIZON 2020 ACTION

POVEZANOST RADA