Multidimensional Design of Anticancer Peptides.

The computer-assisted design and optimization of peptides with selective cancer cell killing activity was achieved through merging the features of anticancer peptides, cell-penetrating peptides, and tumor-homing peptides. Machine-learning classifiers identified candidate peptides that possess the predicted properties. Starting from a template amino acid sequence, peptide cytotoxicity against a range of cancer cell lines was systematically optimized while minimizing the effects on primary human endothelial cells.

Repurposing de novo designed entities reveals phosphodiesterase 3B and cathepsin L modulators.

Using computational bioactivity prediction models we identified phosphodiesterase 3B (PDE3B) and cathepsin L as macromolecular targets of de novo designed compounds. By disclosing the most potent cathepsin L activator known to date, small molecule repurposing by target panel prediction represents a feasible route towards innovative leads for chemical biology and molecular medicine.

Piloting the membranolytic activities of peptides with a self-organizing map.

Antimicrobial peptides (AMPs) show remarkable selectivity toward lipid membranes and possess promising antibiotic potential. Their modes of action are diverse and not fully understood, and innovative peptide design strategies are needed to generate AMPs with improved properties. We present a de novo peptide design approach that resulted in new AMPs possessing low-nanomolar membranolytic activities.

Steering target selectivity and potency by fragment-based de novo drug design.

Kinase inhibitors: Ligand-based de novo design is validated as a viable technology for rapidly generating innovative compounds possessing the desired biochemical profile. The study discloses the discovery of the most selective vascular endothelial growth factor receptor-2 (VEGFR-2) kinase inhibitor (right in scheme) known to date as prime lead for antiangiogenic drug development.