Abou-Gharbia, M. | bioRxiv Science

bioRxiv2026

Pharmacological Stratification of Public Bioactivity Databases: A Reusable, OECD-Anchored Curation and Benchmarking Framework Demonstrated for Opioid Receptors

Public bioactivity databases are heterogeneous not only in measurement type, where binding affinities and functional potencies are reported on different scales, but in pharmacology: the same compound and target can carry agonist, antagonist, or inhibitor records measured through binding displacement, cAMP, {beta}-arrestin, or [35S]GTP{gamma}S readouts that quantify different biological events. Pooling these records produces models whose output is detached from any coherent pharmacological claim. Prior work has standardized bioactivity at scale and quantified the noise from mixing measurement types, but pharmacological mechanism and assay-readout class have not been treated as a primary axis of large-scale curation. This study presents an auditable, OECD-anchored framework that stratifies public records by action type and assay readout before modeling, converting heterogeneous data into externally validated, interpretable QSAR tasks that compose with existing standardization resources rather than replacing them. The framework is demonstrated on the four opioid receptors (MOR, DOR, KOR, and nociceptin/orphanin FQ, NOP). Four public sources were reconciled into 72,148 merged records and 50,977 curated measurements spanning 19,585 compounds, each carrying auditable attributes for source agreement, endpoint meaning, pharmacology class, assay readout, and trust tier. Receptor-level binding tasks formed a compact benchmark with strong locked external performance, including KOR pK (R2 = 0.79, n = 798) and DOR pK (R2 = 0.77, n = 736). Pharmacology- and readout-resolved functional endpoints yielded externally validated strata that pooled labels would obscure, including a MOR antagonist functional-inhibition endpoint (R2 = 0.86, n = 110) and agonist potency endpoints for DOR, KOR, and MOR (R2 up to 0.81). Comparison against a fully pooled baseline shows that pooled models either match stratified models on coherent endpoints or reach a deceptively high R2 on functional-IC50 endpoints by training predominantly on binding-displacement records, so the pooled number predicts affinity rather than functional activity. SHAP attribution indicates that binding and functional potency encode partially distinct structure-activity signals. The dataset contract, not model performance alone, defines the validity and scope of a QSAR claim, and stratification is a precondition for a functional model to support a defensible claim. Curation logic, derived tables, frozen data, and reproducibility artifacts are released.

bioinformatics↗

bioRxiv2020

Comparative Modeling of CDK9 Inhibitors to Explore Selectivity and Structure-Activity Relationships

Cyclin-dependent kinase 9 (CDK9) plays a key role in transcription elongation, and more recently it was also identified as the molecular target of a series of diaminothiazole compounds that reverse epigenetic silencing in a phenotypic assay. To better understand the structural basis underlying these compounds activity and selectivity, we developed a comparative modeling approach that we describe herein. Briefly, this approach draws upon the strong structural conservation across the active conformation of all protein kinases, and their shared pattern of interactions with Type I inhibitors. Because of this, we hypothesized that the large collection of inhibitor/kinase structures available in the Protein Data Bank (PDB) would enable accurate modeling of this diaminothiazole series in complex with each CDK family member. We apply this new comparative modeling pipeline to build each of these structural models, and then demonstrate that these models provide retrospective rationale for the structure-activity relationships that ultimately guided optimization to the lead diaminothiazole compound MC180295 (14e). We then solved the crystal structure of the 14e/CDK9 complex, and found the resulting structure to be in excellent agreement with our corresponding comparative model. Finally, inspired by these models, we demonstrate how structural changes to 14e can be used to rationally tune this compounds selectivity profile. With the emergence of CDK9 as a promising target for therapeutic intervention in cancer, we anticipate that comparative modeling can provide a valuable tool to guide optimization of potency and selectivity of new inhibitors targeting this kinase.

cancer biology↗