An Integrated Virtual Screening Platform to Identify Potent Co‐Assembled Nanodrugs for Cancer Treatment
Bibliographic record
Abstract
Co-assembled nanodrugs provide significant advantages in cancer treatment and drug delivery, yet effective screening methods to identify molecular combinations for co-assembly are lacking. This study presents a screening strategy integrating ligand-based virtual screening (LBVS) and density functional theory (DFT) calculations to explore new molecular combinations with co-assembly capabilities. The accuracy of this screening was validated by synthesizing various co-assembled nanodrugs under mild conditions. Vinpocetine (Vin) and lenvatinib (Len) are representative co-assembly combinations that can directly co-assemble into nanoparticles (NPs) through hydrogen bonding, van der Waals forces, and π-π interactions. These NPs were further functionalized with polyethylene glycol (PEG), resulting in PEG-L/V NPs that exhibited enhanced stability and biocompatibility. In addition, PEG-L/V NPs can respond to acidic conditions and release Vin and Len, working synergistically to induce cell cycle arrest and apoptosis in tumor cells in vitro while also inhibiting xenograft tumor growth in vivo. RNA sequencing (RNA-seq) analysis revealed that the co-assembled nanodrugs exhibited mechanisms that are distinct from those of single drugs. This study demonstrates the feasibility of utilizing a computational approach combining LBVS and DFT to identify small molecules with co-assembly capabilities, leading to innovative anticancer strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".