EXPLOITING PRETRAINED BIOCHEMICAL LANGUAGE MODELS FOR TARGETED DRUG DESIGN

作     者：Uludoğan, Gökçe Ozkirimli, Elif Ulgen, Kutlu O. Karalı, Nilgün Özgür, Arzucan 

作者机构：Department of Computer Engineering Bogazici University Istanbul34342 Turkey Data and Analytics Chapter Pharma International Informatics F. Hoffmann-La Roche AG 4303 Switzerland Department of Pharmaceutical Chemistry Istanbul University Istanbul34116 Turkey 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Molecules 

摘      要：Motivation: The development of novel compounds targeting proteins of interest is one of the most important tasks in the pharmaceutical industry. Deep generative models have been applied to targeted molecular design and have shown promising results. Recently, target-specific molecule generation has been viewed as a translation between the protein language and the chemical language. However, such a model is limited by the availability of interacting protein-ligand pairs. On the other hand, large amounts of unlabeled protein sequences and chemical compounds are available and have been used to train language models that learn useful representations. In this study, we propose exploiting pretrained biochemical language models to initialize (i.e. warm start) targeted molecule generation models. We investigate two warm start strategies: (i) a one-stage strategy where the initialized model is trained on targeted molecule generation (ii) a two-stage strategy containing a pre-finetuning on molecular generation followed by target specific training. We also compare two decoding strategies to generate compounds: beam search and sampling. Results: The results show that the warm-started models perform better than a baseline model trained from scratch. The two proposed warm-start strategies achieve similar results to each other with respect to widely used metrics from benchmarks. However, docking evaluation of the generated compounds for a number of novel proteins suggests that the one-stage strategy generalizes better than the two-stage strategy. Additionally, we observe that beam search outperforms sampling in both docking evaluation and benchmark metrics for assessing compound quality. © 2022, CC BY-NC-SA.