The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Generative Recurrent Networks for De Novo Drug Design
Open accessAbstract
Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest. We present a method for molecular de novo design that utilizes generative recurrent neural networks (RNN) containing long short‐term memory (LSTM) cells. This computational model captured the syntax of molecular representation in terms of SMILES strings with close to perfect accuracy. The learned pattern probabilities can be used for de novo SMILES generation. This molecular design concept eliminates the need for virtual compound library enumeration. By employing transfer learning, we fine‐tuned the RNN′s predictions for specific molecular targets. This approach enables virtual compound design without requiring secondary or external activity prediction, which could introduce error or unwanted bias. The results obtained advocate this generative RNN‐LSTM system for high‐impact use cases, such as low‐data drug discovery, fragment based molecular design, and hit‐to‐lead optimization for diverse drug targets.
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Cites
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Cited by
Schneider, G. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.232 · Cited by 910
Self-citationGuacaMol: Benchmarking Models for de Novo Molecular Design
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Recurrent Neural Network Model for Constructive Peptide Design
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Self-citationEntangled Conditional Adversarial Autoencoder for de Novo Drug Discovery
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Shape-Based Generative Modeling for de Novo Drug Design
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Generative molecular design in low data regimes
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Self-citationCombining generative artificial intelligence and on-chip synthesis for de novo drug design
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Self-citationGenerative machine learning for de novo drug discovery: A systematic review
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RELATION: A Deep Generative Model for Structure-Based De Novo Drug Design
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PCF-VAE: posterior collapse free variational autoencoder for de novo drug design
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“The application of transfer learning is demonstrated by Gupta et al. 18 and Segler et al. 17 in the generation of optimal molecules, particularly in situations when the available training data is restricted.”
Metadata
- Authors
- Gupta, A., Müller, A. T., Huisman, B. J. H., Fuchs, J. A., Schneider, P., & Schneider, G.
- Venue
- Molecular Informatics
- Publisher
- Wiley
- Published
- 2017-11-02
- Volume
- 37
- Issue
- 1-2
- ISSN
- 1868-1743 (ISSN-L)
- 1868-1751
- OpenAlex
- W2765224015
- Cited by
- 441
- References
- 8
- FWCI
- 24.93
- Type
- journal article
- Language
- en
- Open access
- hybrid
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