ArticleJournal of cheminformatics2018
Efficient iterative virtual screening with Apache Spark and conformal prediction.
Article in Journal of cheminformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Deep docking, part 2: an amplified DDU platform for ultra-large virtual screening.Chemical science · 2026Article
- Rapid traversal of vast chemical space using machine learning-guided docking screens.Nature computational science · 2025Article
- Application of Artificial Intelligence-Based Approaches in the Discovery and Development of Protein Kinase Inhibitors (PKIs) Targeting Anticancer Activity.Current topics in medicinal chemistry · 2025Review
- Machine Learning-Driven Data Valuation for Optimizing High-Throughput Screening Pipelines.Journal of chemical information and modeling · 2024Article
- Inverse mapping of quantum properties to structures for chemical space of small organic molecules.Nature communications · 2024Article
- Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR.Nature reviews. Drug discovery · 2024Review
- Framing Apache Spark in life sciences.Heliyon · 2023Review
- Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking.Nature protocols · 2022Review
- Accelerating high-throughput virtual screening through molecular pool-based active learning.Chemical science · 2021Article
- Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery.ACS central science · 2020Article
- Novel applications of Machine Learning in cheminformatics.Journal of cheminformatics · 2018Article
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Authors and funding
7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDocking and scoring large libraries of ligands against target proteins forms the basis of structure-based virtual screening. The problem is trivially parallelizable, and calculations are generally carried out on computer clusters or on large workstations in a brute force manner, by docking and scoring all available ligands. CONTRIBUTION: In this study we propose a strategy that is based on iteratively docking a set of ligands to form a training set, training a ligand-based model on this set, and predicting the remainder of the ligands to exclude those predicted as 'low-scoring' ligands. Then, another set of ligands are docked, the model is retrained and the process is repeated until a certain model efficiency level is reached. Thereafter, the remaining ligands are docked or excluded based on this model. We use SVM and conformal prediction to deliver valid prediction intervals for ranking the predicted ligands, and Apache Spark to parallelize both the docking and the modeling.
resultsWe show on 4 different targets that conformal prediction based virtual screening (CPVS) is able to reduce the number of docked molecules by 62.61% while retaining an accuracy for the top 30 hits of 94% on average and a speedup of 3.7. The implementation is available as open source via GitHub ( https://github.com/laeeq80/spark-cpvs ) and can be run on high-performance computers as well as on cloud resources.
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