{
  "id": 9423905,
  "title": "Leakage-controlled benchmarking reveals generalization limits of deep learning for protein-ligand binding affinity prediction",
  "url": "https://urgent.news/2026/09/23/leakage-controlled-benchmarking-reveals-generalization-limits-of-deep",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-23T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752433v1?rss=1"
  },
  "original_language": "en",
  "account": "A new study has unveiled the limitations of deep learning models when it comes to predicting protein-ligand binding affinity. To tackle the issues of data leakage and inconsistent evaluation, researchers have introduced PLABench, a benchmarking framework that ensures standardized comparisons among various sequence- and structure-based models. The benchmarking was conducted on nine deep learning methods across blind CASP16 targets, leakage-controlled ChEMBL35 sets, and standard datasets like Davis and KIBA. Under strict data split settings and standardized structural input using AlphaFold3, the study found that highly pretrained structure-based models, while achieving the highest accuracy, exhibit significant target-dependent variability. Furthermore, increasing the fidelity of structural inputs from predicted to experimental conformations did not lead to consistent improvements. On the other hand, sequence-based approaches outperformed some structure-based methods on specific targets. The researchers also discovered that protein family-level evaluations showed uneven performance across different families, with substantial inter-model complementarity often hidden by aggregate metrics. These findings underscore the fact that merely scaling up training and using structural input alone are insufficient to ensure cross-target generalization in protein-ligand binding affinity predictions. The study highlights the necessity for context-aware interaction modeling. PLABench, the open-source platform developed for this purpose, aims to support further advancements in this area.",
  "summary": "To address widespread data leakage and inconsistent evaluation in protein-ligand affinity prediction, we introduce PLABench, a leakage-controlled and target-centric benchmarking framework that enables standardized comparison across sequence- and structure-based models. We benchmark nine deep learning methods across blind CASP16 targets, leakage-controlled ChEMBL35 sets, and established Davis and…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}