Small-Molecule Factor Xa Inhibitors: Translational SAR, Assay-Aware Data Quality, and QSAR-Readiness for CADD-Oriented Discovery

Factor Xa (FXa) remains a clinically validated and chemically tractable anticoagulant target despite the therapeutic role of direct oral FXa inhibitors. Contemporary FXa inhibitor literature, however, is heterogeneous in scaffold design, endpoint reporting, assay consistency, translational depth, and suitability for computer-aided drug design (CADD). This review evaluates published series of small-molecule FXa inhibitors through a framework that combines translational structure–activity relationships (SARs), assay-aware data quality, and QSAR-readiness. A structured narrative synthesis focused mainly on post-2014 studies reporting discrete small-molecule or semisynthetic FXa inhibitors. Eligible series were classified as fully synthetic or natural-product-derived/semisynthetic chemotypes, and extraction covered scaffold architecture, potency endpoints, assay context, selectivity, clotting or antithrombotic readouts, PK/ADME, structural clarity, translational context, and extraction confidence. QSAR-readiness was assessed using analog density, congenericity, endpoint quality, assay comparability, activity range, structural interpretability, and curation burden. Fully synthetic chemotypes, particularly anthranilamide-derived and related scaffolds, provided the most coherent and modellable FXa datasets, whereas natural-product-derived and semisynthetic series expanded structural diversity. Many exploratory series, however, were limited by small analog sets, heterogeneous endpoints, incomplete translational characterization, narrow activity ranges, or higher curation burden. The practical value of published FXa inhibitor series, therefore, depends not only on potency but also on whether chemical and biological information can be reconstructed with confidence for reproducible SAR interpretation, local QSAR modeling, AI/ML-enabled CADD reuse, and clinical benchmark-aware prioritization. The QSAR-readiness framework is a critical triage tool, not a substitute for formal validation, distinguishing datasets suitable for curated local modeling from those better suited to qualitative SAR, scaffold inspiration, or translational hypotheses.