Chemical language models enable de novo drug design without the requirement for explicit molecular construction rules. While such models have been applied to generate novel compounds with desired bioactivity, the actual prioritization and selection of the most promising computational designs remains challenging. Herein, we leveraged the probabilities learnt by chemical language models with the beam search algorithm as a model-intrinsic technique for automated molecule design and scoring. Prospective application of this method yielded novel inverse agonists of retinoic acid receptor-related orphan receptors (RORs). Each design was synthesizable in three reaction steps and presented low-micromolar to nanomolar potency towards RORγ. This model-intrinsic sampling technique eliminates the strict need for external compound scoring functions, thereby further extending the applicability of generative artificial intelligence to data-driven drug discovery.
Bibliographical noteFunding Information:
This research was supported by the Swiss National Science Foundation (grant no. 205321_182176 to G.S.), the RETHINK initiative at ETH Zurich, and the Novartis Forschungsstiftung (FreeNovation grant “AI in Drug Discovery” to G.S.). Open access funding enabled and organized by Projekt DEAL.
- de novo design
- deep learning
- drug discovery
- neural network
- nuclear receptor