The DOI matches its Crossref record; 146 of the 146 references it cites were found in a registry or an index.
Advances in De Novo Drug Design: From Conventional to Machine Learning Methods
Open accessAbstract
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.
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Nanotechnology and artificial intelligence to enable sustainable and precision agriculture
Zhang, P., Guo, Z., Ullah, S., Melagraki, G., Afantitis, A., & Lynch, I. (2021) · Nature Plants · doi:10.1038/s41477-021-00946-6 · Cited by 478
Self-citationReinvent 4: Modern AI–driven generative molecule design
Loeffler, H. H., He, J., Tibo, A., Janet, J. P., Voronov, A., Mervin, L. H., & Engkvist, O. (2024) · Journal of Cheminformatics · doi:10.1186/s13321-024-00812-5 · Cited by 319
Molecular Docking: Principles, Advances, and Its Applications in Drug Discovery
Muhammed, M. T., & Aki-Yalcin, E. (2024) · Letters in Drug Design & Discovery · doi:10.2174/1570180819666220922103109 · Cited by 267
Unleashing the power of generative AI in drug discovery
Gangwal, A., & Lavecchia, A. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.103992 · Cited by 195
Bashir, A. K., Victor, N., Bhattacharya, S., Huynh-The, T., Chengoden, R., Yenduri, G., Maddikunta, P. K. R., Pham, Q.-V., Gadekallu, T. R., & Liyanage, M. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3304790 · Cited by 183
Metadata
- Authors
- Mouchlis, V. D., Afantitis, A., Serra, A., Fratello, M., Papadiamantis, A. G., Aidinis, V., Lynch, I., Greco, D., & Melagraki, G.
- Venue
- International Journal of Molecular Sciences
- Publisher
- MDPI AG
- Published
- 2021-02-07
- Volume
- 22
- Issue
- 4
- Pages
- 1676
- ISSN
- 1422-0067 (ISSN-L)
- OpenAlex
- W3127347132
- Cited by
- 353
- References
- 146
- FWCI
- 28.12
- Type
- journal article
- Language
- en
- Open access
- gold
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- Mouchlis, V. D., Afantitis, A., Serra, A., Fratello, M., Papadiamantis, A. G., Aidinis, V., Lynch, I., Greco, D., & Melagraki, G. (2021). Advances in De Novo Drug Design: From Conventional to Machine Learning Methods. International Journal of Molecular Sciences. https://doi.org/10.3390/ijms22041676
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