De Novo Design of Bioactive Small Molecules by Artificial Intelligence

Daniel Merk, Lukas Friedrich, Francesca Grisoni, Gisbert Schneider

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

211 Citaten (Scopus)


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.

Originele taal-2Engels
TijdschriftMolecular Informatics
Nummer van het tijdschrift1-2
StatusGepubliceerd - jan. 2018
Extern gepubliceerdJa

Bibliografische nota

Publisher Copyright:
© 2018 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA.


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