Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)

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Drug discovery: a review on Molecular Property Prediction using Deep Learning Approaches

Authors
Asma Djennad1, *, Reguia Kherbich1, *, Imane Youkana2, Laid Kahloul3, Rachida Saouli3
1Computer Science Department, Mohamed Khider Biskra Univeristy, Biskra, Algeria
2Smart Computer Sciences Laboratory, Department of Computer Sciences, Mohamed Khider Biskra Univeristy, Biskra, Algeria
3LINFI Laboratory, Mohamed Khider Biskra Univeristy, Biskra, Algeria
*Corresponding author. Email: djennad.asma@gmail.com
*Corresponding author. Email: kherbichroukia@gmail.com
Corresponding Authors
Asma Djennad, Reguia Kherbich
Available Online 5 August 2025.
DOI
10.2991/978-94-6463-805-9_8How to use a DOI?
Keywords
Drug discovery; Molecular Property Prediction; Artificial Intelligence; Deep learning
Abstract

Drug discovery is a critical domain that has a profound impact on healthcare and pharmaceutical development. It consists of several steps, which are expensive, time-consuming operation, and characterized by low success rates. Recently, Deep learning (DL) is considered as a powerful tool to accelerate and reduce failure in this field. Various techniques have been applied to numerous drug discovery tasks, such as molecular property prediction (MPP). In this paper, we provide the most useful representation of molecule then we present relevant works that used DL in MPP with a comparative summary, the challenges faced by MPP, and the limitations of DL approaches.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
Series
Advances in Intelligent Systems Research
Publication Date
5 August 2025
ISBN
978-94-6463-805-9
ISSN
1951-6851
DOI
10.2991/978-94-6463-805-9_8How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Asma Djennad
AU  - Reguia Kherbich
AU  - Imane Youkana
AU  - Laid Kahloul
AU  - Rachida Saouli
PY  - 2025
DA  - 2025/08/05
TI  - Drug discovery: a review on Molecular Property Prediction using Deep Learning Approaches
BT  - Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
PB  - Atlantis Press
SP  - 56
EP  - 64
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-805-9_8
DO  - 10.2991/978-94-6463-805-9_8
ID  - Djennad2025
ER  -