Next Article in Journal
Polymorphism of Butyl Ester of Oleanolic Acid—The Dominance of Dispersive Interactions over Electrostatic
Next Article in Special Issue
Protein Tyrosine Phosphatase Receptor Zeta 1 as a Potential Target in Cancer Therapy and Diagnosis
Previous Article in Journal
A Novel E3 Probiotics Formula Restored Gut Dysbiosis and Remodelled Gut Microbial Network and Microbiome Dysbiosis Index (MDI) in Southern Chinese Adult Psoriasis Patients
Previous Article in Special Issue
Cardiac RGS Proteins in Human Heart Failure and Atrial Fibrillation: Focus on RGS4
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation

by
Nikoletta-Maria Koutroumpa
1,2,3,
Konstantinos D. Papavasileiou
1,3,4,
Anastasios G. Papadiamantis
1,3,
Georgia Melagraki
5 and
Antreas Afantitis
1,3,4,*
1
Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1070, Cyprus
2
School of Chemical Engineering, National Technical University of Athens, 157 80 Athens, Greece
3
Division of Data Driven Innovation, Entelos Institute, Larnaca 6059, Cyprus
4
Department of ChemoInformatics, NovaMechanics MIKE., 185 45 Piraeus, Greece
5
Division of Physical Sciences & Applications, Hellenic Military Academy, 166 73 Vari, Greece
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2023, 24(7), 6573; https://doi.org/10.3390/ijms24076573
Submission received: 18 December 2022 / Revised: 24 March 2023 / Accepted: 28 March 2023 / Published: 31 March 2023
(This article belongs to the Special Issue Latest Review Papers in Molecular Pharmacology 2023)

Abstract

The discovery and development of new drugs are extremely long and costly processes. Recent progress in artificial intelligence has made a positive impact on the drug development pipeline. Numerous challenges have been addressed with the growing exploitation of drug-related data and the advancement of deep learning technology. Several model frameworks have been proposed to enhance the performance of deep learning algorithms in molecular design. However, only a few have had an immediate impact on drug development since computational results may not be confirmed experimentally. This systematic review aims to summarize the different deep learning architectures used in the drug discovery process and are validated with further in vivo experiments. For each presented study, the proposed molecule or peptide that has been generated or identified by the deep learning model has been biologically evaluated in animal models. These state-of-the-art studies highlight that even if artificial intelligence in drug discovery is still in its infancy, it has great potential to accelerate the drug discovery cycle, reduce the required costs, and contribute to the integration of the 3R (Replacement, Reduction, Refinement) principles. Out of all the reviewed scientific articles, seven algorithms were identified: recurrent neural networks, specifically, long short-term memory (LSTM-RNNs), Autoencoders (AEs) and their Wasserstein Autoencoders (WAEs) and Variational Autoencoders (VAEs) variants; Convolutional Neural Networks (CNNs); Direct Message Passing Neural Networks (D-MPNNs); and Multitask Deep Neural Networks (MTDNNs). LSTM-RNNs were the most used architectures with molecules or peptide sequences as inputs.
Keywords: drug discovery; drug design; artificial intelligence; machine learning; deep learning; biological evaluation; animal model; in vivo drug discovery; drug design; artificial intelligence; machine learning; deep learning; biological evaluation; animal model; in vivo

Share and Cite

MDPI and ACS Style

Koutroumpa, N.-M.; Papavasileiou, K.D.; Papadiamantis, A.G.; Melagraki, G.; Afantitis, A. A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation. Int. J. Mol. Sci. 2023, 24, 6573. https://doi.org/10.3390/ijms24076573

AMA Style

Koutroumpa N-M, Papavasileiou KD, Papadiamantis AG, Melagraki G, Afantitis A. A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation. International Journal of Molecular Sciences. 2023; 24(7):6573. https://doi.org/10.3390/ijms24076573

Chicago/Turabian Style

Koutroumpa, Nikoletta-Maria, Konstantinos D. Papavasileiou, Anastasios G. Papadiamantis, Georgia Melagraki, and Antreas Afantitis. 2023. "A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation" International Journal of Molecular Sciences 24, no. 7: 6573. https://doi.org/10.3390/ijms24076573

APA Style

Koutroumpa, N.-M., Papavasileiou, K. D., Papadiamantis, A. G., Melagraki, G., & Afantitis, A. (2023). A Systematic Review of Deep Learning Methodologies Used in the Drug Discovery Process with Emphasis on In Vivo Validation. International Journal of Molecular Sciences, 24(7), 6573. https://doi.org/10.3390/ijms24076573

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop