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Orsi, Markus; Reymond, Jean-Louis (2024). Can large language models predict antimicrobial peptide activity and toxicity? RSC Medicinal Chemistry, 15(6), pp. 2030-2036. Royal Society of Chemistry 10.1039/d4md00159a
Orsi, Markus; Reymond, Jean-Louis (2024). One chiral fingerprint to find them all. Journal of cheminformatics, 16(53) Springer 10.1186/s13321-024-00849-6
Orsi, Markus; Loh, Boon Shing; Weng, Cheng; Ang, Wee Han; Frei, Angelo (2024). Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes. Angewandte Chemie. International edition, 63(10), e202317901. Wiley 10.1002/anie.202317901
Orsi, Markus; Probst, Daniel; Schwaller, Philippe; Reymond, Jean-Louis (2023). Alchemical analysis of FDA approved drugs. Digital discovery, 2(5), pp. 1289-1296. Royal Society of Chemistry 10.1039/d3dd00039g
Cai, Xingguang; Capecchi, Alice; Olcay, Başak; Orsi, Markus; Javor, Sacha; Reymond, Jean-Louis (2023). Exploring the Sequence Space of Antimicrobial Peptide Dendrimers. Israel journal of chemistry, 63(10-11) Wiley-VCH 10.1002/ijch.202300096
Cai, Xingguang; Orsi, Markus; Capecchi, Alice; Köhler, Thilo; van Delden, Christian; Javor, Sacha; Reymond, Jean-Louis (2022). An intrinsically disordered antimicrobial peptide dendrimer from stereorandomized virtual screening. Cell reports. Physical science, 3(12), pp. 1-15. Cell Press 10.1016/j.xcrp.2022.101161
Zakharova, Elena; Orsi, Markus; Capecchi, Alice; Reymond, Jean-Louis (2022). Machine Learning Guided Discovery of Non-Hemolytic Membrane Disruptive Anti-Cancer Peptides. ChemMedChem, 17(17), e202200291. Wiley-VCH 10.1002/cmdc.202200291
