This paper presents a novel approach for predicting molecular properties with high accuracy without the need for extensive pre-training. Employing ensemble learning and supervised fine-tuning of BERT, RoBERTa, and XLNet, our method demonstrates significant effectiveness compared to existing advanced models. Crucially, it addresses the issue of limited computational resources faced by experimental groups, enabling them to accurately predict molecular properties. This innovation provides a cost-effective and resource-efficient solution, potentially advancing further research in the molecular domain.
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