Computer‑aided drug design (CADD) plays a central role in modern drug development, expediting and streamlining the discovery process across a wide range of therapeutic targets. This dissertation focuses on the application of CADD to support the identification of novel inhibitors for two cancer‑relevant targets: Cytochrome P450 (CYP) 4A11 and protein phosphatase 1 (PP1). Cancer remains a major threat to human health, highlighting the need for advanced therapies with innovative pharmacological mechanisms. To date, no market‑approved anti‑cancer drugs targeting CYP4A11 or PP1 have been successfully developed, making both proteins highly promising targets. In this work, tailored computational strategies were employed to discover and optimize potential therapeutic candidates for these targets. In the first section, human CYP4A11, an enzyme critically involved in hepatic fatty acid oxidation, is analyzed. This reaction increases the generation of reactive oxygen species (ROS) and contributes to the pathogenesis of metabolic dysfunction‑associated steatotic liver disease (MASLD), leading to an increased risk of developing hepatocellular carcinoma (HCC). To identify novel CYP4A11 inhibitors, key structure‑based 3D pharmacophore interactions were extracted to enable virtual screening, followed by sequential filtering based on substructures and interaction patterns. The binding behavior of the most promising virtual hits was further assessed through both static and dynamic evaluations within the CYP4A11 binding site. The top candidates were experimentally tested using a luminescence‑based assay in permeabilized fission yeast cells. This workflow led to the identification of a novel set of imidazole‑ or triazole‑substituted small‑molecule inhibitors. Among these, the most potent candidates, C2 and C4, show nanomolar potency. In the second section, the serine/threonine phosphatase PP1 is investigated. This enzyme cat‑ alyzes the dephosphorylation of numerous essential proteins, thereby maintaining cellular homeostasis and regulating critical signaling pathways. Consequently, PP1 inhibition can induce cell death and is under investigation as a potential therapeutic mechanism in oncology. Microcystins (MCs) are high‑affinity PP1 binders that represent promising lead structures for anticancer drug development. However, their clinical application is hampered by challenges in selectivity and associated toxicity. In this dissertation, we present a structure‑guided analysis of PP1 aimed at improving the binding affinity of MC derivatives, conducted in collaboration with Professor Timo Niedermeyer, a leading expert in MC research with over a decade of experimental experience. We examined the flexibility of PP1 with a focus on its binding site and discovered a novel subpocket. To exploit this subpocket, semi‑synthetic MC derivatives were developed, and their binding modes were investigated in silico. Building on this analysis, a covalent docking protocol was applied to generate a shortlist of promising, optimized MC derivatives with potentially enhanced binding profiles. In summary, this thesis employs and develops integrated computational and experimental approaches for preclinical cancer drug discovery. Through molecular docking 3D pharmacophores, molecular dynamics simulations and experimental in vitro testing, we identified novel, potent CYP4A11 inhibitors and designed optimized PP1 binders.
