Journal
INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES
Volume 22, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/ijms22041676
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
Funding
- Republic of Cyprus through the Research and Innovation Foundation
- Academy of Finland [322761]
- H2020 EU research infrastructure for nanosafety project NanoCommons [731032]
- EU H2020 nanoinformatics project NanoSolveIT [814572]
- European Regional Development Fund
- [CONCEPT/0618/0031]
- [ENTERPRISES/0916/14]
- [ENTERPRISES/0618/0122]
Ask authors/readers for more resources
De novo drug design is a process of generating novel molecular structures using computational methods, with traditional approaches including structure-based and ligand-based design. Artificial intelligence and machine learning have a positive impact in this field.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available