A team of researchers in China has combined machine learning, molecular docking and molecular dynamics simulations into a single computational pipeline that can sift through tens of thousands of chemical compounds and pull out a handful of genuinely promising cancer drug candidates. Writing in the journal Molecular Diversity, the group led by Yihui Jiang of Hangzhou Lin’an Traditional Chinese Medicine Hospital describes how their integrated virtual screening workflow identified a compound, HY-18,623, that shuts down two closely related cancer-driving enzymes with nanomolar potency. The finding is a striking demonstration of how artificial intelligence methods, when layered carefully with physics-based simulation, can compress the early stages of drug discovery from years of laboratory grinding into a focused computational campaign.
The biological target at the heart of the study is a pair of enzymes known as cyclin-dependent kinases 4 and 6, or CDK4 and CDK6. These kinases act as gatekeepers of the cell division cycle, driving the transition from the G1 phase, when cells grow and prepare, into S phase, when DNA is replicated. When the CDK4/6 machinery is dysregulated, cells can escape normal growth controls and proliferate uncontrollably, a hallmark of many malignancies. The importance of these enzymes is underscored by the clinical success of approved CDK4/6 inhibitors such as palbociclib, ribociclib and abemaciclib, which have transformed the treatment landscape for hormone receptor-positive breast cancer and are being explored in other tumor types, including lung cancer.
Yet the existing drugs are not the end of the story. Resistance mechanisms emerge, side effects limit some patients, and clinicians and researchers continue to call for chemically diverse scaffolds, meaning molecules with fundamentally different structural backbones, that can potently inhibit both CDK4 and CDK6 at the same time. Finding such dual inhibitors by traditional high-throughput screening is expensive and slow, which is precisely why the research team turned to computer-aided drug discovery. Their strategy was to build a computational funnel: a wide opening that could swallow an entire chemical library and progressively narrower stages that would discard weak candidates until only the most promising molecules remained.
The first stage of the funnel was ligand-based machine learning. Rather than requiring knowledge of the target protein’s three-dimensional structure, ligand-based models learn from the known activities of compounds that have already been tested. The researchers assembled curated datasets from the ChEMBL database, a public repository of bioactivity data, gathering 265 compounds with measured activity against CDK4 and 402 against CDK6. Each molecule was converted into a numerical representation called an extended-connectivity fingerprint, specifically the ECFP4 format, which encodes the local atomic environments of a molecule in a way that machine learning algorithms can digest. Fingerprint-based descriptors of this kind have become a workhorse of computational chemistry because they capture subtle structural features that often correlate with biological activity.
With the data prepared, the team benchmarked multiple machine learning algorithms to see which could best predict inhibitory potency. The winner was a Bayesian Ridge regressor, a statistical learning method that combines ridge regression with Bayesian regularization, making it robust against overfitting on modestly sized datasets. Trained on the ECFP4 fingerprints, the model achieved cross-validated coefficients of determination, R-squared values, of 0.731 plus or minus 0.022 for CDK4 and 0.721 plus or minus 0.070 for CDK6. In practical terms, this means the model could explain roughly seventy percent of the variance in compound potency from molecular structure alone, a level of predictive accuracy strong enough to make meaningful prioritization decisions. The consistency of performance across both targets was crucial, because the goal was a dual inhibitor, and a model that excelled at one kinase but stumbled on the other would have undermined the entire approach.
The trained machine learning filter was then deployed against a library of 22,823 compounds. The Bayesian Ridge model scored every molecule for its likely activity against CDK4 and CDK6, allowing the researchers to discard the vast majority of the library computationally before any expensive calculation was run. This is where the economics of the approach become compelling: instead of assaying or docking tens of thousands of compounds, the team could concentrate structural modeling resources on the small fraction that the statistical model deemed most promising. The survivors of the machine learning filter then entered the second stage of the funnel, molecular docking against both CDK4 and CDK6.
Docking is a structure-based technique that uses three-dimensional models of the target proteins, typically derived from experimentally determined structures in the Protein Data Bank, to predict how a small molecule physically fits into the binding pocket of the enzyme. The docking calculations, performed at standard precision, evaluated the geometric complementarity and predicted binding energy of each candidate within the ATP-binding sites of both kinases. By requiring candidates to dock well against both CDK4 and CDK6, the researchers enforced dual-target affinity at the structural level, complementing the statistical predictions of the machine learning stage. This dual-target docking refinement whittled the prioritized list down to three candidate hits selected for biochemical evaluation in the laboratory.
The experimental results validated the computational strategy emphatically. Of the three candidates tested, HY-18,623 emerged as a potent dual inhibitor, with a half-maximal inhibitory concentration, IC50, of 3.5 nanomolar against CDK4 and 17.4 nanomolar against CDK6. Values in the low nanomolar range represent the kind of potency typically associated with advanced lead compounds, and achieving it against both kinases simultaneously with a single molecule is exactly what the field has been seeking. The result suggests that the computational funnel did not merely enrich for plausible binders but genuinely identified a molecule with drug-development potential.
To understand why HY-18,623 binds so tightly, the researchers turned to the third pillar of their workflow: molecular dynamics simulations. Docking provides a static snapshot of a predicted binding pose, but proteins and ligands in solution are constantly in motion, and a pose that looks perfect in a single frame may fall apart within nanoseconds. The team ran 200-nanosecond molecular dynamics simulations of the CDK4 and CDK6 complexes with HY-18,623, tracking the stability of the bound state over time using metrics such as root mean square deviation of the atomic positions. The simulations revealed that the compound maintains persistent hydrogen bonds with the so-called hinge residues of both kinases, Val96 in CDK4 and Val101 in CDK6. Hinge-region hydrogen bonding is a canonical anchor of kinase inhibitor binding, and its persistence throughout the simulations indicates a stable, high-affinity binding mode. Binding free energy analyses further quantified the favorable thermodynamics of the interaction, corroborating the experimental potency measurements.
Beyond the specific discovery of HY-18,623, the study offers a template for how modern drug discovery campaigns can be organized. The sequential integration of ligand-based machine learning, structure-based docking and dynamics-based validation creates a hierarchy of evidence, in which each stage interrogates the candidates from a different angle: statistical structure-activity relationships, physical fit within the binding site, and dynamic stability of the complex. The researchers have also made their data publicly available through a GitHub repository, supporting the open-science ethos that is increasingly shaping kinase drug discovery. For a field in which approved CDK4/6 inhibitors have already changed the course of breast cancer treatment, yet resistance and toxicity continue to motivate the search for alternatives, the arrival of a computationally discovered, nanomolar dual inhibitor with a structurally characterized binding mode is a development worth watching. HY-18,623 now stands as a promising lead candidate for further therapeutic development in oncology, and the workflow that produced it as a potentially reusable engine for discovering the next generation of kinase inhibitors.
Subject of Research: Computational virtual screening combining machine learning, docking and molecular dynamics to discover dual CDK4/6 inhibitors for cancer therapy
Article Title: Harnessing machine learning, docking and molecular dynamics for the virtual screening of compounds as CDK4/6 dual inhibitors
Article References: Wang, Y., Fang, L., Liu, Q., Zhang, Z., Xu, C., & Jiang, Y. (2026). Harnessing machine learning, docking and molecular dynamics for the virtual screening of compounds as CDK4/6 dual inhibitors. Molecular Diversity. https://doi.org/10.1007/s11030-026-11716-x
Image Credits: AI Generated
DOI: 10.1007/s11030-026-11716-x
Keywords: CDK4, CDK6, virtual screening, machine learning, molecular docking, molecular dynamics, drug discovery, kinase inhibitors, cancer, ChEMBL, ECFP4 fingerprints, HY-18,623
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Nathaniel Bowman. (September 26, 2026). AI-Powered Virtual Screening Uncovers Potent New Dual CDK4/6 Cancer Inhibitor. Scienmag. https://scienmag.com/ai-powered-virtual-screening-uncovers-potent-new-dual-cdk4-6-cancer-inhibitor/
Nathaniel Bowman. “AI-Powered Virtual Screening Uncovers Potent New Dual CDK4/6 Cancer Inhibitor.” Scienmag, 26 September 2026, https://scienmag.com/ai-powered-virtual-screening-uncovers-potent-new-dual-cdk4-6-cancer-inhibitor/. Accessed 26 September 2026.
Nathaniel Bowman. “AI-Powered Virtual Screening Uncovers Potent New Dual CDK4/6 Cancer Inhibitor.” Scienmag. September 26, 2026. https://scienmag.com/ai-powered-virtual-screening-uncovers-potent-new-dual-cdk4-6-cancer-inhibitor/
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Tags: AI-powered virtual screeningcancercancer drug discoveryCDK4CDK6ChEMBLcomputational drug screening pipelinedrug discoverydual CDK4/6 inhibitorsearly-stage cancer drug developmentECFP4 fingerprintsenzyme inhibition for cancer treatmentHY-18,623integrated AI and physics-based modelingkinase inhibitorsMachine learningmachine learning in pharmacologymolecular dockingmolecular docking and dynamics simulationsmolecular dynamicsnanomolar potency compoundsrapid drug candidate identificationtargeted cancer therapyvirtual screening
