2023/10/09 by Pengcheng Xu, Xu, Pengcheng, Tao Feng +7
Biochemistry, Genetics and Molecular Biology · Computer Science · Materials Science · #Artificial Intelligence (cs.AI) #Chemical Synthesis and Analysis #Computational Drug Discovery Methods #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science
paper · pdf · doi:10.48550/arxiv.2310.05365
openalex publication_date 2023/10/09 · openalex created_date 2023/10/13 · openalex updated_date 2026/07/28
In this work, we introduce a method to fine-tune a Transformer-based generative model for molecular de novo design. Leveraging the superior sequence learning capacity of Transformers over Recurrent Neural Networks (RNNs), our model can generate molecular structures with desired properties effectively. In contrast to the traditional RNN-based models, our proposed method exhibits superior performance in generating compounds predicted to be active against various biological targets, capturing long-term dependencies in the molecular structure sequence. The model's efficacy is demonstrated across numerous tasks, including generating analogues to a query structure and producing compounds with particular attributes, outperforming the baseline RNN-based methods. Our approach can be used for scaffold hopping, library expansion starting from a single molecule, and generating compounds with high predicted activity against biological targets.