Computer-aided discovery of CDK16 inhibitors: a docking-augmented machine learning regression modelling approach
Clinical Snapshot
PICO Framework
| P — Population | CDK16 protein target and chemical compound libraries (NCI and OpnMe databases) |
| I — Intervention | Docking-augmented machine learning regression modelling approach for drug discovery |
| C — Comparator | Traditional computational drug discovery methods without docked pose augmentation |
| O — Outcomes | CDK16 inhibitor potency (IC50 values), model performance metrics, and identification of novel inhibitors |
Bottom Line
This computational drug discovery study presents a novel approach combining molecular docking augmentation with machine learning to identify CDK16 inhibitors. The researchers successfully identified two compounds with micromolar inhibitory activity against CDK16, a kinase implicated in aggressive cancers. While the computational methodology appears robust and the experimental validation promising, this represents very early-stage research. The identified compounds require extensive further development including selectivity profiling, toxicity assessment, and optimization before any clinical consideration. For Australian clinicians, this work contributes to the pipeline of potential cancer therapeutics but has no immediate clinical implications. The approach may accelerate future drug discovery efforts for this understudied target.
Key Findings
P Value: Not reported in abstract
Effect Size: IC50 values: 3.5 µM (H_28/BI-831266) and 5.8 µM (H_33/BI-1282)
Primary Outcome: Identification of novel CDK16 inhibitors with micromolar potency
Nnt Or Sensitivity: Model successfully identified 2 active compounds from virtual screening
Confidence Interval: Not reported in abstract
Clinical Application
Early-stage research requiring extensive development before clinical application Relevant to Australian cancer patients; would require TGA approval pathway for therapeutic development Patients with CDK16-overexpressing cancers (triple-negative breast, lung, prostate cancer)
Abstract
Cyclin-dependent kinase 16 (CDK16), a serine/threonine protein kinase, is a critical regulator of cell cycle progression, vesicle trafficking, and apoptosis. Its dysregulation is implicated in the progression of aggressive cancers, including triple-negative breast, lung, and prostate cancer, where its overexpression correlates with poor prognosis. Despite its therapeutic promise, CDK16 remains an understudied kinase lacking selective inhibitors, underscoring the need for innovative discovery approaches. This study introduces a novel computational framework that leverages multiple docked poses to augment datasets for regression-based machine learning (ML) models targeting CDK16 inhibition. This data augmentation strategy incorporated docking scores, ligand-receptor contact fingerprints (LRCFs), and conformation-sensitive physicochemical descriptors as input features. To our knowledge, this is the first application of docked pose augmentation for a regression-based drug discovery model. A systematic evaluation of eight ML algorithms across multiple docking score consensus levels identified Gradient Boosted Trees as the optimal learner. The Genetic Function Algorithm was integrated to select a minimal set of descriptors, which improved model generalizability. The resulting validated ML-QSAR model guided the generation of a robust pharmacophore model, which was used for virtual screening of the NCI and OpnMe databases. Subsequent in vitro LanthaScreen kinase assays confirmed two novel and potent CDK16 inhibitors: compound H_28 (OpnMe code: BI-831266) with an IC50 of 3.5 µM, and H_33 (OpnMe code: BI-1282) with an IC50 of 5.8 µM. These findings highlight the efficacy of integrating docked pose augmentation with regression modeling and experimental validation, offering a robust framework for accelerating the discovery of targeted anticancer agents.
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