A study in 3 Biotech introduced ESMTmP, a multiscale multitask model that improves prediction of protein melting temperatures. The model uses a frozen 300-million-parameter Cambrian encoder plus residual temporal convolutions and attention-based fusion, trained on 30,076 observations. Reported performance includes root mean square error of 7.42 °C and mean absolute error of 5.64 °C, alongside a mean area under the ROC curve of 0.9462 across temperature thresholds and no recorded monotonicity violations. The authors used monotonicity regularization to align predicted ordering with temperature thresholds. In practice, better thermal stability prediction can shorten protein engineering cycles by prioritizing thermostable candidates before experimental screening, supporting both industrial enzyme development and biomedical research pipelines.