Advances in de novo drug design: from conventional to machine learning methods

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Advances in de novo drug design : from conventional to machine learning methods. / Mouchlis, Varnavas D.; Afantitis, Antreas; Serra, Angela; Fratello, Michele; Papadiamantis, Anastasios G.; Aidinis, Vassilis; Lynch, Iseult; Greco, Dario; Melagraki, Georgia.

In: International Journal of Molecular Sciences, Vol. 22, No. 4, 1676, 07.02.2021.

Research output: Contribution to journalReview articlepeer-review

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Mouchlis, Varnavas D. ; Afantitis, Antreas ; Serra, Angela ; Fratello, Michele ; Papadiamantis, Anastasios G. ; Aidinis, Vassilis ; Lynch, Iseult ; Greco, Dario ; Melagraki, Georgia. / Advances in de novo drug design : from conventional to machine learning methods. In: International Journal of Molecular Sciences. 2021 ; Vol. 22, No. 4.

Bibtex

@article{35cd552a5158477c9ce6e4da5de7d9bb,
title = "Advances in de novo drug design: from conventional to machine learning methods",
abstract = "De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure‐based and ligand‐based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement‐learning architectures. This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencod-ers. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine‐learning methodologies and highlights hot topics for further de-velopment.",
keywords = "Artificial intelligence, Artificial neural networks, Autoencoders, Convolutional neural networks, De novo drug design, Deep reinforcement learning, Generative adversarial networks, Machine learning, Recurrent neural networks",
author = "Mouchlis, {Varnavas D.} and Antreas Afantitis and Angela Serra and Michele Fratello and Papadiamantis, {Anastasios G.} and Vassilis Aidinis and Iseult Lynch and Dario Greco and Georgia Melagraki",
note = "Funding Information: Funding: V.D.M. and A.A. acknowledge support from: CONCEPT/0618/0031, ENTER‐ PRISES/0916/14 and ENTERPRISES/0618/0122 projects, which were co‐funded by the European Re‐ gional Development Fund and the Republic of Cyprus through the Research and Innovation Foun‐ dation. DG received support from the Academy of Finland (grant agreement 322761). This work was supported via the H2020 EU research infrastructure for nanosafety project NanoCommons (Grant Agreement No. 731032), the EU H2020 nanoinformatics project NanoSolveIT (Grant Agree‐ ment No. 814572). Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",
year = "2021",
month = feb,
day = "7",
doi = "10.3390/ijms22041676",
language = "English",
volume = "22",
journal = "International Journal of Molecular Sciences",
issn = "1661-6596",
publisher = "MDPI",
number = "4",

}

RIS

TY - JOUR

T1 - Advances in de novo drug design

T2 - from conventional to machine learning methods

AU - Mouchlis, Varnavas D.

AU - Afantitis, Antreas

AU - Serra, Angela

AU - Fratello, Michele

AU - Papadiamantis, Anastasios G.

AU - Aidinis, Vassilis

AU - Lynch, Iseult

AU - Greco, Dario

AU - Melagraki, Georgia

N1 - Funding Information: Funding: V.D.M. and A.A. acknowledge support from: CONCEPT/0618/0031, ENTER‐ PRISES/0916/14 and ENTERPRISES/0618/0122 projects, which were co‐funded by the European Re‐ gional Development Fund and the Republic of Cyprus through the Research and Innovation Foun‐ dation. DG received support from the Academy of Finland (grant agreement 322761). This work was supported via the H2020 EU research infrastructure for nanosafety project NanoCommons (Grant Agreement No. 731032), the EU H2020 nanoinformatics project NanoSolveIT (Grant Agree‐ ment No. 814572). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

PY - 2021/2/7

Y1 - 2021/2/7

N2 - De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure‐based and ligand‐based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement‐learning architectures. This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencod-ers. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine‐learning methodologies and highlights hot topics for further de-velopment.

AB - De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure‐based and ligand‐based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement‐learning architectures. This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencod-ers. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine‐learning methodologies and highlights hot topics for further de-velopment.

KW - Artificial intelligence

KW - Artificial neural networks

KW - Autoencoders

KW - Convolutional neural networks

KW - De novo drug design

KW - Deep reinforcement learning

KW - Generative adversarial networks

KW - Machine learning

KW - Recurrent neural networks

UR - http://www.scopus.com/inward/record.url?scp=85100512934&partnerID=8YFLogxK

U2 - 10.3390/ijms22041676

DO - 10.3390/ijms22041676

M3 - Review article

C2 - 33562347

VL - 22

JO - International Journal of Molecular Sciences

JF - International Journal of Molecular Sciences

SN - 1661-6596

IS - 4

M1 - 1676

ER -