Domain adaptable language modeling of chemical compounds identifies potent pathoblockers for Pseudomonas aeruginosa.

Computational techniques for predicting molecular properties are emerging as pivotal components for streamlining drug development, optimizing time, and financial investments. Here, we introduce ChemLM, a transformer language model-based approach for this task. ChemLM further leverages self-supervised domain adaptation on chemical molecules to enhance its predictive performance across new domains of interest. Within the framework of ChemLM, chemical compounds are conceptualized as sentences composed of distinct chemical ‘words’, which are employed for training a specialized chemical language model. On the standard benchmark datasets, ChemLM has either matched or surpassed the performance of current state-of-the-art methods. Furthermore, we evaluated the effectiveness of ChemLM in identifying highly potent pathoblockers targeting Pseudomonas aeruginosa (PA), a pathogen that has shown an increased prevalence of multidrug-resistant strains and has been identified as a critical priority for the development of new medications. ChemLM demonstrated significantly higher accuracy in identifying highly potent pathoblockers against PA when compared to state-of-the-art approaches. An intrinsic evaluation demonstrated the consistency of the chemical language model’s representation concerning chemical properties. Our results from benchmarking, experimental data, and intrinsic analysis of the ChemLM space confirm the wide applicability of ChemLM for enhancing molecular property prediction within the chemical domain.