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  "text": "NovoMolGen: Rethinking Molecular Language Model Pretraining\n\n\"there remains limited understanding regarding how standard language  modeling practices such as textual representations, tokenization  strategies, model size, and dataset scale impact molecular generation  performance. In this work, we systematically investigate these critical  aspects by introducing NovoMolGen, a family of transformer-based  foundation models pretrained on 1.5 billion molecules for de-novo  molecule generation. Through extensive empirical analyses, we identify a  weak correlation between performance metrics measured during  pretraining and actual downstream performance, revealing important  distinctions between molecular and general NLP training dynamics.  NovoMolGen establishes new state-of-the-art results, substantially  outperforming prior Mol-LLMs and specialized generative models in both  unconstrained and goal-directed molecular generation tasks\"",
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