Generative machine learning for de novo drug discovery: A systematic review
Abstract
Recent research on artificial intelligence indicates that machine learning algorithms can auto-generate novel drug-like molecules. Generative models have revolutionized de novo drug discovery, rendering the explorative process more efficient. Several model frameworks and input formats have been proposed to enhance the performance of intelligent algorithms in generative molecular design. In this systematic literature review of experimental articles and reviews over the last five years, machine learning models, challenges associated with computational molecule design along with proposed solutions, and molecular encoding methods are discussed. A query-based search of the PubMed, ScienceDirect, Springer, Wiley Online Library, arXiv, MDPI, bioRxiv, and IEEE Xplore databases yielded 87 studies. Twelve additional studies were identified via citation searching. Of the articles in which machine learning was implemented, six prominent algorithms were identified: long short-term memory recurrent neural networks (LSTM-RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), adversarial autoencoders (AAEs), evolutionary algorithms, and gated recurrent unit (GRU-RNNs). Furthermore, eight central challenges were designated: homogeneity of generated molecular libraries, deficient synthesizability, limited assay data, model interpretability, incapacity for multi-property optimization, incomparability, restricted molecule size, and uncertainty in model evaluation. Molecu…
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Techniques such as generative adversarial networks combined with reinforcement learning allow the intelligent generation of molecular graph representations.
“Techniques such as GANs combined with reinforcement learning allow the intelligent generation of molecular graph representations [22].”
From Generative AI in medical practice: in-depth exploration of privacy and security challenges (2024) · cites this paper as [22]
Cites
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Deep learning enables rapid identification of potent DDR1 kinase inhibitors
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Evolutionary algorithms: A critical review and its future prospects
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Generative AI in medical practice: in-depth exploration of privacy and security challenges
Chen, Y., & Esmaeilzadeh, P. (2024) · Journal of Medical Internet Research · doi:10.2196/53008 · Cited by 353 · see citing works
“Techniques such as GANs combined with reinforcement learning allow the intelligent generation of molecular graph representations [22].”
Computer especially AI-assisted drug virtual screening and design in traditional Chinese medicine
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- Authors
- Martinelli, D. D.
- Corresponding author
- Martinelli, Dominic D.
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2022-06
- Volume
- 145
- Pages
- 105403
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W4221062742
- Cited by
- 177
- References
- 10
- Citations by year
- 2026: 35 · 2025: 67 · 2024: 44 · 2023: 23 · 2022: 8
- FWCI
- 3.12
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
- Keywords
- generative machine learning, de novo drug discovery, generative molecular design, molecular encoding, variational autoencoder, generative adversarial networks, adversarial autoencoder, recurrent neural networks, evolutionary algorithms, multi-property optimization, model interpretability
- MeSH terms
- Machine Learning, Artificial Intelligence, Drug Design, Neural Networks, Computer, Drug Discovery
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