ArticleBiology2020
Prediction of Antimalarial Drug-Decorated Nanoparticle Delivery Systems with Random Forest Models.
Article in Biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Systematic review on the application of machine learning to quantitative structure-activity relationship modeling against Plasmodium falciparum.Molecular diversity · 2022Pooled it
- Computational Phenotypic Drug Discovery for Anticancer Chemotherapy: PTML Modeling of Multi-Cell Inhibitors of Colorectal Cancer Cell Lines.International journal of molecular sciences · 2025Article
- In Silico Approach for Early Antimalarial Drug Discovery: De Novo Design of Virtual Multi-Strain Antiplasmodial Inhibitors.Microorganisms · 2025Article
- Perturbation-Theory Machine Learning for Multi-Target Drug Discovery in Modern Anticancer Research.Current issues in molecular biology · 2025Review
- SynBioNanoDesign: pioneering targeted drug delivery with engineered nanomaterials.Journal of nanobiotechnology · 2025Review
- In Silico Approach for Antibacterial Discovery: PTML Modeling of Virtual Multi-Strain Inhibitors AgainstPharmaceuticals (Basel, Switzerland) · 2025Article
- Perturbation-theory machine learning for mood disorders: virtual design of dual inhibitors of NET and SERT proteins.BMC chemistry · 2025Article
- Ligand-Based Approach for Multi-Target Drug Discovery: PTML Modeling of Triple-Target Inhibitors.Current topics in medicinal chemistry · 2025Article
- NANO.PTML model for read-across prediction of nanosystems in neurosciences. computational model and experimental case of study.Journal of nanobiotechnology · 2024Article
- Forecasting and mapping dengue fever epidemics in China: a spatiotemporal analysis.Infectious diseases of poverty · 2024Article
- On the additive artificial intelligence-based discovery of nanoparticle neurodegenerative disease drug delivery systems.Beilstein journal of nanotechnology · 2024Article
- Multi-Condition QSAR Model for the Virtual Design of Chemicals with Dual Pan-Antiviral and Anti-Cytokine Storm Profiles.ACS omega · 2022Article
- PTML Modeling for Pancreatic Cancer Research: In Silico Design of Simultaneous Multi-Protein and Multi-Cell Inhibitors.Biomedicines · 2022Article
- Similarity-Based Method with Multiple-Feature Sampling for Predicting Drug Side Effects.Computational and mathematical methods in medicine · 2022Article
- Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases.Frontiers in cellular and infection microbiology · 2022Review
- In Silico Drug Repurposing for Anti-Inflammatory Therapy: Virtual Search for Dual Inhibitors of Caspase-1 and TNF-Alpha.Biomolecules · 2021Article
- Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning.International journal of molecular sciences · 2021Article
- Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
Abstract
Drug-decorated nanoparticles (DDNPs) have important medical applications. The current work combined Perturbation Theory with Machine Learning and Information Fusion (PTMLIF). Thus, PTMLIF models were proposed to predict the probability of nanoparticle-compound/drug complexes having antimalarial activity (against Plasmodium). The aim is to save experimental resources and time by using a virtual screening for DDNPs. The raw data was obtained by the fusion of experimental data for nanoparticles with compound chemical assays from the ChEMBL database. The inputs for the eight Machine Learning classifiers were transformed features of drugs/compounds and nanoparticles as perturbations of molecular descriptors in specific experimental conditions (experiment-centered features). The resulting dataset contains 107 input features and 249,992 examples. The best classification model was provided by Random Forest, with 27 selected features of drugs/compounds and nanoparticles in all experimental conditions considered. The high performance of the model was demonstrated by the mean Area Under the Receiver Operating Characteristics (AUC) in a test subset with a value of 0.9921 ± 0.000244 (10-fold cross-validation). The results demonstrated the power of information fusion of the experimental-centered features of drugs/compounds and nanoparticles for the prediction of nanoparticle-compound antimalarial activity. The scripts and dataset for this project are available in the open GitHub repository.
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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.