ArticleInternational journal of molecular sciences2021
VirtualFlow Ants-Ultra-Large Virtual Screenings with Artificial Intelligence Driven Docking Algorithm Based on Ant Colony Optimization.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 35 citations in OpenAlex.
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Predicting Inhibition of CDK2 with SAnDReS: The Application of Machine Learning to Navigate the Scoring Function Space.Current medicinal chemistry · 2025Review
- Identification of putative allosteric inhibitors of BCKDK via virtual screening and biological evaluation.Journal of enzyme inhibition and medicinal chemistry · 2024Article
- Natural flavonoid glycosides Chrysosplenosides I & A rejuvenate intestinal stem cell aging via activation of PPARγ signaling.Life medicine · 2024Article
- DockOpt: A Tool for Automatic Optimization of Docking Models.Journal of chemical information and modeling · 2024Article
- Integrating QSAR modelling and deep learning in drug discovery: the emergence of deep QSAR.Nature reviews. Drug discovery · 2024Review
- Large-Scale Docking in the Cloud.Journal of chemical information and modeling · 2023Article
- Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology.International journal of molecular sciences · 2023Review
- Emerging frontiers in virtual drug discovery: From quantum mechanical methods to deep learning approaches.Current opinion in chemical biology · 2022Review
- Drugsniffer: An Open Source Workflow for Virtually Screening Billions of Molecules for Binding Affinity to Protein Targets.Frontiers in pharmacology · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
Abstract
The docking program PLANTS, which is based on ant colony optimization (ACO) algorithm, has many advanced features for molecular docking. Among them are multiple scoring functions, the possibility to model explicit displaceable water molecules, and the inclusion of experimental constraints. Here, we add support of PLANTS to VirtualFlow (VirtualFlow Ants), which adds a valuable method for primary virtual screenings and rescoring procedures. Furthermore, we have added support of ligand libraries in the MOL2 format, as well as on the fly conversion of ligand libraries which are in the PDBQT format to the MOL2 format to endow VirtualFlow Ants with an increased flexibility regarding the ligand libraries. The on the fly conversion is carried out with Open Babel and the program SPORES. We applied VirtualFlow Ants to a test system involving KEAP1 on the Google Cloud up to 128,000 CPUs, and the observed scaling behavior is approximately linear. Furthermore, we have adjusted several central docking parameters of PLANTS (such as the speed parameter or the number of ants) and screened 10 million compounds for each of the 10 resulting docking scenarios. We analyzed their docking scores and average docking times, which are key factors in virtual screenings. The possibility of carrying out ultra-large virtual screening with PLANTS via VirtualFlow Ants opens new avenues in computational drug discovery.
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Registered trials
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