Journal
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
Volume 36, Issue 5, Pages 363-371Publisher
SPRINGER
DOI: 10.1007/s10822-021-00392-8
Keywords
Multi-target activity; Deep learning; Generative modeling; Structure-promiscuity relationships; Multi-target ligand design
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Funding
- Projekt DEAL
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The study found that generative neural networks can be utilized to create multi-target compounds, and fine-tuning the model can systematically identify, distinguish, and construct new multi-target compounds.
Exploring the origin of multi-target activity of small molecules and designing new multi-target compounds are highly topical issues in pharmaceutical research. We have investigated the ability of a generative neural network to create multi-target compounds. Data sets of experimentally confirmed multi-target, single-target, and consistently inactive compounds were extracted from public screening data considering positive and negative assay results. These data sets were used to fine-tune the REINVENT generative model via transfer learning to systematically recognize multi-target compounds, distinguish them from single-target or inactive compounds, and construct new multi-target compounds. During fine-tuning, the model showed a clear tendency to increasingly generate multi-target compounds and structural analogs. Our findings indicate that generative models can be adopted for de novo multi-target compound design.
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