Up to 90% of patients with high-grade serous ovarian cancer (HGSC) will develop resistance to platinum-based chemotherapy, posing substantial therapeutic challenges due to a lack of universally druggable targets. Leveraging BenevolentAI's artificial intelligence (AI)-driven approach to target discovery, we screened potential AI-predicted therapeutic targets mapped to unapproved tool compounds in patient-derived 3D models. This identified TNIK, which is modulated by NCB-0846, as a novel target for platinum-resistant HGSC. Targeting by this compound demonstrated efficacy across both in vitro and ex vivo organoid platinum-resistant models. Additionally, NCB-0846 treatment effectively decreased Wnt activity, a known driver of platinum resistance; however, we found that these effects were not solely mediated by TNIK inhibition. Comprehensive AI, in silico, and in vitro analyses revealed CDK9 as another key target driving NCB-0846's efficacy. Interestingly, TNIK and CDK9 co-expression positively correlated, and chromosomal gains in both served as prognostic markers for poor patient outcomes. Combined knockdown of TNIK and CDK9 markedly diminished downstream Wnt targets and reduced chemotherapy-resistant cell viability. Furthermore, we identified CDK9 as a novel mediator of canonical Wnt activity, providing mechanistic insights into the combinatorial effects of TNIK and CDK9 inhibition and offering a new understanding of NCB-0846 and CDK9 inhibitor function. Our findings identified the TNIK-CDK9 axis as druggable targets mediating platinum resistance and cell viability in HGSC. With AI at the forefront of drug discovery, this work highlights how to ensure that AI findings are biologically relevant by combining compound screens with physiologically relevant models, thus supporting the identification and validation of potential drug targets.