DNN-PP: A novel Deep Neural Network approach and its applicability in drug-related property prediction

Magdalena Wiercioch; Johannes Kirchmair

doi:10.1016/j.eswa.2022.119055

DNN-PP: A novel Deep Neural Network approach and its applicability in drug-related property prediction

Magdalena Wiercioch (Corresponding author)
, Johannes Kirchmair

Department of Pharmaceutical Sciences

Publications: Contribution to journal › Article › Peer Reviewed

Original language	English
Article number	119055
Number of pages	14
Journal	Expert systems with applications
Volume	213
Issue number	B
DOIs	https://doi.org/10.1016/j.eswa.2022.119055
Publication status	Published - 1 Mar 2023

Funding

This work is a part of broader research funded under the Iwanowska Program of the Polish National Agency for Academic Exchange (NAWA) - a decision no. PPN/IWA/2018/1/00094/DEC/1 . This research was also supported by the National Science Centre (Poland) Grants No. 2016/21/N/ST6/01019 . This publication has been partially funded from the SciMat Priority Research Area budget under the program “Excellence Initiative - Research University” at the Jagiellonian University.

Austrian Fields of Science 2012

301207 Pharmaceutical chemistry
102001 Artificial intelligence

Keywords

Deep learning
Representation learning
Drug discovery
Molecular properties prediction
Deep Neural Networks
BIOLOGICAL-ACTIVITY
SELECTION

Access to Document

10.1016/j.eswa.2022.119055

Cite this

@article{8d3934adfac042a99d7d50ddbdf77609,

title = "DNN-PP: A novel Deep Neural Network approach and its applicability in drug-related property prediction",

keywords = "Deep learning, Representation learning, Drug discovery, Molecular properties prediction, Deep Neural Networks, BIOLOGICAL-ACTIVITY, SELECTION",

author = "Magdalena Wiercioch and Johannes Kirchmair",

note = "Publisher Copyright: {\textcopyright} 2022 Elsevier Ltd",

year = "2023",

month = mar,

day = "1",

doi = "10.1016/j.eswa.2022.119055",

language = "English",

volume = "213",

journal = "Expert systems with applications",

issn = "0957-4174",

publisher = "Elsevier",

number = "B",

}

TY - JOUR

T1 - DNN-PP

T2 - A novel Deep Neural Network approach and its applicability in drug-related property prediction

AU - Wiercioch, Magdalena

AU - Kirchmair, Johannes

PY - 2023/3/1

Y1 - 2023/3/1

KW - Deep learning

KW - Representation learning

KW - Drug discovery

KW - Molecular properties prediction

KW - Deep Neural Networks

KW - BIOLOGICAL-ACTIVITY

KW - SELECTION

UR - https://www.scopus.com/pages/publications/85140914063

U2 - 10.1016/j.eswa.2022.119055

DO - 10.1016/j.eswa.2022.119055

M3 - Article

SN - 0957-4174

VL - 213

JO - Expert systems with applications

JF - Expert systems with applications

IS - B

M1 - 119055

ER -

DNN-PP: A novel Deep Neural Network approach and its applicability in drug-related property prediction

Funding

Austrian Fields of Science 2012

Keywords

Access to Document

Other files and links

Fingerprint

Cite this