Chronic IL-6 trans-signalling via the gp130 receptor drives breast cancer progression, metastasis, and drug resistance, yet small-molecule gp130 inhibitors remain limited. This study presents an integrated computational–experimental pipeline to discover novel gp130 inhibitors. Structural models of the IL-6 trans-signalling complex will be built and validated, and large libraries from DrugBank and SANCDB will be prepared using cheminformatics tools. Virtual screening will combine BioSolveIT LeadIT/FlexX docking, SeeSAR pose refinement and lead optimisation, and infiniSee analogue expansion, followed by machine-learning ADMET filtering. Top candidates will undergo flexible docking confirmation and triplicate membrane molecular dynamics simulations with binding energy analysis. Prioritised compounds will be validated using gp130-binding and pSTAT3-inhibition assays in breast cancer cell models.
Yannick intends to achieve the following milestones:
- Target modeling and virtual screening completion
- Hit optimization, ADMET filtering, and dynamic validation
- Experimental validation and functional proof-of-concept