An Integrated Consensus Machine Learning and Structure-Based Workflow for the Discovery of Novel Tankyrase 1 Inhibitors

Abstract

Background: Tankyrase 1 (TNKS1) is a poly(ADP-ribose) polymerase involved in Wnt/β-catenin signaling, telomere maintenance, and genomic stability, making it an attractive therapeutic target in oncology. This study aimed to develop and apply an integrated computational workflow to identify novel TNKS1 inhibitor candidates. Methods: A curated dataset of experimentally validated TNKS1 inhibitors and property-matched DUD-E decoys was used to develop a consensus supervised machine learning (ML) model prioritization framework for ligand-based virtual screening, integrating Morgan fingerprints with three complementary classifiers. The model screened more than 700,000 compounds, and prioritized hits were evaluated by structure-based virtual screening (SBVS), Prime MM-GBSA binding free-energy refinement, and 500 ns molecular dynamics simulations (MDs). The top candidates were subsequently tested in an in vitro TNKS1 enzymatic inhibition assay. Results: The consensus ML framework prioritized 670 compounds, yielding five candidates for experimental testing. Compound 3 displayed the most favorable computational profile and was experimentally confirmed as a TNKS1 inhibitor candidate, exhibiting approximately 80% TNKS1 inhibition at 0.1 μM, whereas the remaining candidates showed only limited activity. Conclusions: The proposed workflow efficiently reduced a large chemical space to a focused set of TNKS1 inhibitor candidates while substantially reducing the experimental screening burden. Compound 3 represents a promising starting point for future structure–activity relationship studies and lead optimization in the context of TNKS1 inhibition. Moreover, this work highlights the value of integrating consensus ML, SBVS, and experimental validation to accelerate early-stage hit discovery for TNKS1 and other therapeutic targets.