Chem42: a Family of chemical Language Models for Target-aware Ligand Generation

Revolutionizing drug discovery demands more than just understanding molecular interactions - it requires generative models that can design novel ligands tailored to specific biological targets. While chemical Language Models (cLMs) have made strides in learning molecular properties, most fail to incorporate target-specific insights, restricting their ability to drive de-novo ligand generation. Chem42, a cutting-edge family of generative chemical Language Models, is designed to bridge this gap. By integrating atomic-level interactions with multimodal inputs from Prot42, a complementary protein Language Model, Chem42 achieves a sophisticated cross-modal representation of molecular structures, interactions, and binding patterns. This innovative framework enables the creation of structurally valid, synthetically accessible ligands with enhanced target specificity. Evaluations across diverse protein targets confirm that Chem42 surpasses existing approaches in chemical validity, target-aware design, and predicted binding affinity. By reducing the search space of viable drug candidates, Chem42 could accelerate the drug discovery pipeline, offering a powerful generative AI tool for precision medicine. Our Chem42 models set a new benchmark in molecule property prediction, conditional molecule generation, and target-aware ligand design. The models are publicly available atthis http URL.

@article{singh2025_2503.16563,
  title={ Chem42: a Family of chemical Language Models for Target-aware Ligand Generation },
  author={ Aahan Singh and Engin Tekin and Maryam Nadeem and Nancy A. ElNaker and Mohammad Amaan Sayeed and Natalia Vassilieva and Boulbaba Ben Amor },
  journal={arXiv preprint arXiv:2503.16563},
  year={ 2025 }
}