@article{bibcite_682,
  author = {C. M. Matter and A. Caflisch},
  title = {Fine-Tuning a Transformer Model for METTL3 Lead Optimization},
  abstract = {<p>Transformers are machine learning models originally developed to translate between natural languages. Recently, a transformer model was trained on knowledge of medicinal chemistry, i.e., matched molecular pairs of nearly a million bioactive compounds from the ChEMBL database. Here, we customize (i.e., fine-tune) the pretrained model to enhance the affinity and/or metabolic stability of a series of inhibitors of methyltransferase-like protein 3 (METTL3). We first fine-tune the transformer model using a data set of about 500 METTL3 inhibitors with known binding affinities and validate it by retrospective analysis. Then, we fine-tune the original transformer model to simultaneously optimize binding affinity and metabolic stability in a prospective application. Two of the five METTL3 inhibitors predicted by the multiobjective optimized model show low-nanomolar potency and higher stability than the lead compound of the chemical series used for fine-tuning.</p>},
  year = {2026},
  journal = {ACS Bio \& Med Chem Au},
  volume = {6},
  pages = {160-167},
  month = {2026 Jan 20},
  url = {https://pubs.acs.org/doi/10.1021/acsbiomedchemau.5c00198},
  doi = {10.1021/acsbiomedchemau.5c00198},
  language = {eng},
}