ArticleJournal of cheminformatics2024
Consensus holistic virtual screening for drug discovery: a novel machine learning model approach.
Article in Journal of cheminformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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
15 citing papers in PubMed.
- A Multi-Scale Computational Framework for Anti-Persister Tuberculosis Drug Discovery.Chemical biology & drug design · 2026Review
- µORScreen: a lightweight consensus modeling framework for µ-opioid receptor ligand prediction and virtual screening.RSC advances · 2026Article
- HTS-Oracle X: AI-Guided Prospective Discovery of Small Molecule Immune Checkpoint Binders.ACS medicinal chemistry letters · 2026Article
- Identifying Antibiotic Effects of Investigational Drugs on Commensal Bacteria with Machine Learning.ACS pharmacology & translational science · 2026Article
- Real-World Pharmacotherapy-Driven Cardiovascular Risk Prediction Using Interpretable Machine Learning and Jordanian EHR Data.Medical sciences (Basel, Switzerland) · 2026Article
- Integrating traditional and modern approaches for comprehensive pharmacophore map validation in drug discovery.Journal of computer-aided molecular design · 2026Review
- Research progress of artificial intelligence in high-throughput drug screening.Frontiers in pharmacology · 2026Review
- Calibrated Weighted Rank Aggregation for Virtual Screening Independently Rediscovers Privileged Vitamin D Receptor Ligand Scaffolds.Computational and structural biotechnology journal · 2026Article
- Article
- Improving Machine Learning Classification Predictions through SHAP and Features Analysis Interpretation.Journal of chemical information and modeling · 2025Article
- The effect of electronic prescription systems on pharmacy performance through evaluation of existing infrastructure in Kerman Iran.Scientific reports · 2025Article
- Machine learning optimization of environmental factors influencing biomass and nutritional composition in local algal species.Royal Society open science · 2025Article
- Hydroxyethylamine based analog targets microtubule assembly: an in silico study for anti-cancerous drug development.Scientific reports · 2024Article
- Cheminformatic Identification of Tyrosyl-DNA Phosphodiesterase 1 (Tdp1) Inhibitors: A Comparative Study of SMILES-Based Supervised Machine Learning Models.Journal of personalized medicine · 2024Article
- Enzyme ChE, cholinergic therapy and molecular docking: Significant considerations and future perspectives.International journal of immunopathology and pharmacologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In drug discovery, virtual screening is crucial for identifying potential hit compounds. This study aims to present a novel pipeline that employs machine learning models that amalgamates various conventional screening methods. A diverse array of protein targets was selected, and their corresponding datasets were subjected to active/decoy distribution analysis prior to scoring using four distinct methods: QSAR, Pharmacophore, docking, and 2D shape similarity, which were ultimately integrated into a single consensus score. The fine-tuned machine learning models were ranked using the novel formula "w_new", consensus scores were calculated, and an enrichment study was performed for each target. Distinctively, consensus scoring outperformed other methods in specific protein targets such as PPARG and DPP4, achieving AUC values of 0.90 and 0.84, respectively. Remarkably, this approach consistently prioritized compounds with higher experimental PIC
Indexed as
Identifiers
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.