De Novo Design of Bioactive Small Molecules by Artificial Intelligence

• Published in: Molecular informatics Vol. 37; no. 1-2
• Main Authors: Merk, Daniel, Friedrich, Lukas, Grisoni, Francesca, Schneider, Gisbert
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01.01.2018; John Wiley and Sons Inc
• Subjects: Artificial intelligence; Automation; Bioactive compounds; Biological activity; Biological effects; Communication; Communications; Computer applications; Constitution; Deep Learning; Design; Drug Design; drug discovery; Generative artificial intelligence; HEK293 Cells; Humans; Machine learning; medicinal chemistry; Molecular Docking Simulation; Neural networks; nuclear receptor; Peroxisome Proliferator-Activated Receptors - agonists; Peroxisome Proliferator-Activated Receptors - chemistry; Quantitative Structure-Activity Relationship; Recurrent neural networks; Retinoid X Receptors - agonists; Retinoid X Receptors - chemistry; Small Molecule Libraries - chemical synthesis; Small Molecule Libraries - pharmacology; Strings; Transfer learning; nuclear receptor; Automation; drug discovery; machine learning; medicinal chemistry
• ISSN: 1868-1743; 1868-1751; 1868-1751
• DOI: 10.1002/minf.201700153

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.