Exploration into the Antitumor Potential of Chiral Oligopeptides: a Comprehensive Study Encompassing Design, Synthesis, In Silico Target Screening and Experimental Validation

Peptides are assuming an increasingly critical role in the fields of medicine and cosmetics. Artificial intelligence–assisted drug screening has significantly improved the efficiency and economic feasibility of drug development. Tryptophan and proline, two common natural chiral amino acids, are extensively utilized in peptide owing to their structural restrictive effects. d -type non-natural amino acids, which correspond to their natural counterparts, enhance the antienzymatic hydrolysis capability of peptides and enable unique target interactions. In this study, we designed and synthesized a series of chiral oligopeptides incorporating d -tryptophan and d -proline, and systematically identified their potential molecular targets through an integrated approach combining artificial intelligence-assisted in silico target prediction with experimental biophysical binding assays. In vitro biological activity assays demonstrated that these chiral compounds exhibited potent antitumor effects against human liver cancer BEL-7404 cells, potentially inducing autophagy and apoptosis. The potential targets of the oligopeptides were initially screened using pharmacophore mapping (PharmMapper) in conjunction with reverse molecular docking (GalaxySagittarius-AF), which revealed that poly(ADP-ribose) polymerase 1 (PARP1) is a key target for these compounds. The target was subsequently validated using biolayer interferometry (BLI, dose–response curves), enzyme-linked immunosorbent assay, molecular docking, and molecular dynamics simulations. The quantitative structure–activity relationship (QSAR) of the prepared peptides was analyzed using PaDEL-Descriptor to provide a rational basis for future peptide-based drug design. This study presents a straightforward, cost-effective, and rapid methodology for the chemical synthesis, biological activity validation, and AI-assisted target identification of antitumor chiral linear oligopeptides.