Generative artificial intelligence offers a fresh view on molecular design. We present the first-time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine-tuned on recognizing retinoid X and peroxisome proliferator-activated receptor agonists. We synthesized five top-ranking compounds designed by the generative model. Four of the compounds revealed nanomolar to low-micromolar receptor modulatory activity in cell-based assays. Apparently, the computational model intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective de novo molecular design, and demonstrate the potential of these methods for future medicinal chemistry

Merk, D., Friedrich, L., Grisoni, F., Schneider, G. (2018). De Novo Design of Bioactive Small Molecules by Artificial Intelligence. MOLECULAR INFORMATICS, 37(1) [10.1002/minf.201700153].

De Novo Design of Bioactive Small Molecules by Artificial Intelligence

Grisoni, F;
2018

Abstract

Generative artificial intelligence offers a fresh view on molecular design. We present the first-time prospective application of a deep learning model for designing new druglike compounds with desired activities. For this purpose, we trained a recurrent neural network to capture the constitution of a large set of known bioactive compounds represented as SMILES strings. By transfer learning, this general model was fine-tuned on recognizing retinoid X and peroxisome proliferator-activated receptor agonists. We synthesized five top-ranking compounds designed by the generative model. Four of the compounds revealed nanomolar to low-micromolar receptor modulatory activity in cell-based assays. Apparently, the computational model intrinsically captured relevant chemical and biological knowledge without the need for explicit rules. The results of this study advocate generative artificial intelligence for prospective de novo molecular design, and demonstrate the potential of these methods for future medicinal chemistry
Articolo in rivista - Articolo scientifico
Automation; drug discovery; machine learning; medicinal chemistry; nuclear receptor
English
2018
37
1
1700153
none
Merk, D., Friedrich, L., Grisoni, F., Schneider, G. (2018). De Novo Design of Bioactive Small Molecules by Artificial Intelligence. MOLECULAR INFORMATICS, 37(1) [10.1002/minf.201700153].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/180607
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