Miniprotein binders hold a great interest as a class of drugs that bridges the gap between monoclonal antibodies and small molecule drugs [1]. Like monoclonal antibodies, they can be designed to bind to therapeutic targets with high affinity, but they are more stable and easier to produce and to administer [2]. Thus, efficient and robust methods are expected to reduce the cost and time required for tailor-made inhibitor design. Here, we present a structure-based computational generic approach for de novo miniprotein inhibitor design. Specifically, we describe step-by-step the implementation of the approach for the design of miniprotein binders against the SARS-CoV-2 coronavirus, using available structural data on the SARS-CoV-2 spike receptor binding domain (RBD) in interaction with its native target, the human receptor ACE2 [3]. Structural data being increasingly accessible around many protein-protein interaction systems, this method might be applied to the design of miniprotein binders against numerous therapeutic targets. The computational pipeline exploits provable and deterministic artificial intelligence-based protein design methods [4,5], with some recent additions in terms of binding energy estimation, multistate design and diverse library generation [6-8]. References [1] Vazquez-Lombardi R, Phan TG, Zimmermann C, et al (2015) Challenges and opportunities for non-antibody scaffold drugs. Drug Discov Today 20:1271–1283. https://doi.org/10.1016/j.drudis.2015.09.004 [2] Crook ZR, Nairn NW, Olson JM (2020) Miniproteins as a Powerful Modality in Drug Development. Trends Biochem Sci 45:332–346. https://doi.org/10.1016/j.tibs.2019.12.008 [3] Wang Q, Zhang Y, Wu L, et al (2020) Structural and Functional Basis of SARS-CoV-2 Entry by Using Human ACE2. Cell 181:894-904.e9. https://doi.org/10.1016/j.cell.2020.03.045 [4] Traoré S, Allouche D, André I, et al (2013) A new framework for computational protein design through cost function network optimization. Bioinformatics 29:2129–2136. https://doi.org/10.1093/bioinformatics/btt374 [5] Traoré S, Allouche D, André I, et al (2017) Deterministic Search Methods for Computational Protein Design. Methods Mol Biol Clifton NJ 1529:107–123. https://doi.org/10.1007/978-1-4939-6637-0_4 [6] Vucinic J, Simoncini D, Ruffini M, et al (2020) Positive multistate protein design. Bioinformatics, 36:122–130. https://doi.org/10.1093/bioinformatics/btz497 [7] Ruffini M, Vucinic J, Givry S de, et al (2021) Guaranteed Diversity Quality for the Weighted CSP. Network Based Computational Protein Design Methods. Algorithms 2021, 14, 168. https://doi.org/10.3390/a14060168 [8] Viricel C, de Givry S, Schiex T, Barbe S (2018) Cost function network-based design of protein-protein interactions: predicting changes in binding affinity. Bioinformatics 34:2581–2589. https://doi.org/10.1093/bioinformatics/bty092