ArticleJournal of chemical information and modeling2021
Machine Learning-Enabled Pipeline for Large-Scale Virtual Drug Screening.
Article in Journal of chemical information and modeling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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.
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.
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Who cites it
16 citing papers in PubMed, 40 citations in OpenAlex.
- Organ-on-chip bridging musculoskeletal disease modeling and precision therapeutic discovery.Trends in biotechnology · 2026Review
- 7-Prenyloxycoumarins as Promising Antileishmanial Agents: In Vitro, In Vivo, and In Silico Evaluation AgainstPharmaceuticals (Basel, Switzerland) · 2026Article
- MolGAN-QRL: a hybrid framework for molecule generation using quantum-enhanced reinforcement learning.Journal of cheminformatics · 2026Article
- Stability-Driven Pose Prediction and Ligand Design via Contact Persistence Analysis from Molecular Dynamics Simulations.Computational and structural biotechnology journal · 2026Article
- Machine-learning and simulation identify food-derived xanthine oxidase inhibitors with in-vitro activity.Scientific reports · 2025Article
- HIt Discovery using docking ENriched by GEnerative Modeling (HIDDEN GEM): A novel computational workflow for accelerated virtual screening of ultra-large chemical libraries.Molecular informatics · 2024Article
- ChatGPT in Drug Discovery: A Case Study on Anticocaine Addiction Drug Development with Chatbots.Journal of chemical information and modeling · 2023Article
- ChatGPT in Drug Discovery: A Case Study on Anti-Cocaine Addiction Drug Development with Chatbots.ArXiv · 2023Article
- Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Review
- TargIDe: a machine-learning workflow for target identification of molecules with antibiofilm activity against Pseudomonas aeruginosa.Journal of computer-aided molecular design · 2023Article
- POH1/Rpn11/PSMD14: a journey from basic research in fission yeast to a prognostic marker and a druggable target in cancer cells.British journal of cancer · 2022Review
- Identification of Potential Cytochrome P450 3A5 Inhibitors: An Extensive Virtual Screening through Molecular Docking, Negative Image-Based Screening, Machine Learning and Molecular Dynamics Simulation Studies.International journal of molecular sciences · 2022Article
- Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches.Current opinion in chemical biology · 2022Review
- Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term.Briefings in bioinformatics · 2022Article
- Application of random forest based on semi-automatic parameter adjustment for optimization of anti-breast cancer drugs.Frontiers in oncology · 2022Article
- MO-MEMES: A method for accelerating virtual screening using multi-objective Bayesian optimization.Frontiers in medicine · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Virtual screening is receiving renewed attention in drug discovery, but progress is hampered by challenges on two fronts: handling the ever-increasing sizes of libraries of drug-like compounds and separating true positives from false positives. Here, we developed a machine learning-enabled pipeline for large-scale virtual screening that promises breakthroughs on both fronts. By clustering compounds according to molecular properties and limited docking against a drug target, the full library was trimmed by 10-fold; the remaining compounds were then screened individually by docking; and finally, a dense neural network was trained to classify the hits into true and false positives. As illustration, we screened for inhibitors against RPN11, the deubiquitinase subunit of the proteasome, and a drug target for breast cancer.
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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.