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Review

Advances in De Novo Drug Design: From Conventional to Machine Learning Methods

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Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1046, Cyprus
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Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland
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BioMEdiTech Institute, Tampere University, 33520 Tampere, Finland
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School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK
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Institute for Bioinnovation, Biomedical Sciences Research Center Alexander Fleming, Fleming 34, 16672 Athens, Greece
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Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
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Finnish Center for Alternative Methods (FICAM), Tampere University, 33520 Tampere, Finland
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Division of Physical Sciences & Applications, Hellenic Military Academy, 16672 Vari, Greece
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Authors to whom correspondence should be addressed.
Academic Editor: M. Natália D.S. Cordeiro
Int. J. Mol. Sci. 2021, 22(4), 1676; https://doi.org/10.3390/ijms22041676
Received: 16 December 2020 / Revised: 31 January 2021 / Accepted: 31 January 2021 / Published: 7 February 2021
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 ma-chine 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 em-ployed 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 autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and high-lights hot topics for further development. View Full-Text
Keywords: de novo drug design; artificial intelligence; machine learning; deep reinforcement learning; artificial neural networks; recurrent neural networks; convolutional neural networks; generative adversarial networks; autoencoders de novo drug design; artificial intelligence; machine learning; deep reinforcement learning; artificial neural networks; recurrent neural networks; convolutional neural networks; generative adversarial networks; autoencoders
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MDPI and ACS Style

Mouchlis, V.D.; Afantitis, A.; Serra, A.; Fratello, M.; Papadiamantis, A.G.; Aidinis, V.; Lynch, I.; Greco, D.; Melagraki, G. Advances in De Novo Drug Design: From Conventional to Machine Learning Methods. Int. J. Mol. Sci. 2021, 22, 1676. https://doi.org/10.3390/ijms22041676

AMA Style

Mouchlis VD, Afantitis A, Serra A, Fratello M, Papadiamantis AG, Aidinis V, Lynch I, Greco D, Melagraki G. Advances in De Novo Drug Design: From Conventional to Machine Learning Methods. International Journal of Molecular Sciences. 2021; 22(4):1676. https://doi.org/10.3390/ijms22041676

Chicago/Turabian Style

Mouchlis, Varnavas D., Antreas Afantitis, Angela Serra, Michele Fratello, Anastasios G. Papadiamantis, Vassilis Aidinis, Iseult Lynch, Dario Greco, and Georgia Melagraki. 2021. "Advances in De Novo Drug Design: From Conventional to Machine Learning Methods" International Journal of Molecular Sciences 22, no. 4: 1676. https://doi.org/10.3390/ijms22041676

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