Articlenpj drug discovery2025
FeatureDock for protein-ligand docking guided by physicochemical feature-based local environment learning using transformer.
Article in npj drug discovery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- In silico discovery of natural and synthetic inhibitors targeting AKT1 in prostate cancer.Molecular diversity · 2026Article
- A survey of transformer and LLM-based architectures, workflows, and systems in drug discovery.Journal of computer-aided molecular design · 2026Review
- Beyond the canonical: The role of post-transcriptional regulation in drug-target interaction prediction.PLoS computational biology · 2026Article
- Article
- Uncertainty quantification enables reliable deep learning for protein-ligand binding affinity prediction.Scientific reports · 2025Article
- Molecule generation for target protein binding with hierarchical consistency diffusion model.Chemical science · 2025Article
- Leveraging AI to explore structural contexts of post-translational modifications in drug binding.Journal of cheminformatics · 2025Article
- Leveraging AI to Explore Structural Contexts of Post-Translational Modifications in Drug Binding.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
Abstract
Molecular docking, the task of predicting the binding structures between a protein and a small molecule ligand, plays a significant role in structural-based drug discovery. In recent years, numerous deep learning-based methods for molecular docking have emerged. State-of-the-art approaches such as DiffDock formulate the docking problem using diffusion generative models, exhibiting superior performance than traditional docking algorithms. However, despite the strong performance of these deep learning-based docking methods in predicting binding poses, they often lack a well-defined scoring function. This limitation poses challenges in effectively distinguishing between the strong and weak inhibitors during virtual screening. To address this limitation, we introduce FeatureDock, a transformer-based deep learning framework, which can leverage chemical features from protein local environments to accurately predict the protein-ligand binding poses as well as achieve a strong scoring power for virtual screening. We demonstrate the robustness of FeatureDock on Cyclin-Dependent Kinase 2 and Angiotensin-converting enzyme compound datasets.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.