Holistic in silico developability assessment of novel classes of small proteins using publicly available sequence-based predictors
D. A. M. Pais, J.-P. A. Mayer, K. Felderer, M. B. Batalha, T. Eichner, S. T. Santos, R. Kumar, S. D. Silva, H. Kaufmann
This paper develops machine learning models to predict the developability of Anticalin proteins using sequence-based descriptors, achieving low prediction errors and validated through cross-validation.
bioRxiv · Nov 10, 2023Biochemistry