Abstract
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.
Highlights
Previous studies showed that chemical language models (CLMs),[13,14] in particular generative deep learning models trained on SMILES strings, can generate novel molecules with experimentally validated bioactivity.[9,15,16]
Given the probabilities learnt by a CLM, a vast number of SMILES strings could in theory be sampled
The beam search score allows to rank the de novo designs according to the probability of their SMILES tokens
Summary
Generative deep learning,[1,2] that is, a class of machine learning models able to generate new data, can be applied to computationally design pharmacologically active compounds de novo.[3,4,5] Deep learning-based molecular design algorithms can extract high-level molecular features from “raw” molecular representations,[6,7,8,9,10] such as molecular graphs and the Simplified Molecular Input Line Entry System (SMILES, Figure 1 a),[11] potentially allowing them to access unexplored regions of the chemical space.[12].
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