ArticleJournal of cheminformatics2021
ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions.
Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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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.
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Who cites it
8 citing papers in PubMed.
- Reproducibility, validation, and failure modes across classical and AI-driven molecular docking.Journal of computer-aided molecular design · 2026Review
- Reindeer: a protein-ligand feature generator software for machine learning algorithms.BMC bioinformatics · 2026Article
- Machine learning models for prediction of (Pro)cathepsin-glycosaminoglycan binding free energies based on molecular structure.Computational and structural biotechnology journal · 2026Article
- Computer-Aided Drug Discovery and Design: Recent Advances and Future Prospects.Methods in molecular biology (Clifton, N.J.) · 2024Review
- TB-IECS: an accurate machine learning-based scoring function for virtual screening.Journal of cheminformatics · 2023Article
- XLPFE: A Simple and Effective Machine Learning Scoring Function for Protein-Ligand Scoring and Ranking.ACS omega · 2022Article
- The impact of cross-docked poses on performance of machine learning classifier for protein-ligand binding pose prediction.Journal of cheminformatics · 2021Article
- Resources and computational strategies to advance small molecule SARS-CoV-2 discovery: Lessons from the pandemic and preparing for future health crises.Computational and structural biotechnology journal · 2021Review
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Virtual screening (VS) based on molecular docking has emerged as one of the mainstream technologies of drug discovery due to its low cost and high efficiency. However, the scoring functions (SFs) implemented in most docking programs are not always accurate enough and how to improve their prediction accuracy is still a big challenge. Here, we propose an integrated platform called ASFP, a web server for the development of customized SFs for structure-based VS. There are three main modules in ASFP: (1) the descriptor generation module that can generate up to 3437 descriptors for the modelling of protein-ligand interactions; (2) the AI-based SF construction module that can establish target-specific SFs based on the pre-generated descriptors through three machine learning (ML) techniques; (3) the online prediction module that provides some well-constructed target-specific SFs for VS and an additional generic SF for binding affinity prediction. Our methodology has been validated on several benchmark datasets. The target-specific SFs can achieve an average ROC AUC of 0.973 towards 32 targets and the generic SF can achieve the Pearson correlation coefficient of 0.81 on the PDBbind version 2016 core set. To sum up, the ASFP server is a powerful tool for structure-based VS.
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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.