ReviewDrug discovery today2017
Systemic QSAR and phenotypic virtual screening: chasing butterflies in drug discovery.
Review in Drug discovery today, 2017. 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, 33 citations in OpenAlex.
- Artificial Intelligence-Guided Phenotypic Drug Repurposing Against Streptococcus pneumoniae.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Application of Data-Centric Supervised Machine Learning to Predict Phenotypic Activity Against Clinically Relevant Stages ofPharmaceutics · 2025Article
- Reinforcement learning for systems pharmacology-oriented and personalized drug design.Expert opinion on drug discovery · 2022Review
- Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges.Methods in molecular biology (Clifton, N.J.) · 2022Review
- G protein-coupled receptors: structure- and function-based drug discovery.Signal transduction and targeted therapy · 2021Review
- A Multi-Objective Approach for Anti-Osteosarcoma Cancer Agents Discovery through Drug Repurposing.Pharmaceuticals (Basel, Switzerland) · 2020Article
- Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches.Molecules (Basel, Switzerland) · 2020Review
- Article
- Prevention of Deficit in Neuropsychiatric Disorders through Monitoring of Arsenic and Its Derivatives as Well as Through Bioinformatics and Cheminformatics.International journal of molecular sciences · 2019Review
- The Light and Dark Sides of Virtual Screening: What Is There to Know?International journal of molecular sciences · 2019Review
- Cell fishing: A similarity based approach and machine learning strategy for multiple cell lines-compound sensitivity prediction.PloS one · 2019Article
- Finding the molecular scaffold of nuclear receptor inhibitors through high-throughput screening based on proteochemometric modelling.Journal of cheminformatics · 2018Article
- Article
- A desirability-based multi objective approach for the virtual screening discovery of broad-spectrum anti-gastric cancer agents.PloS one · 2018Article
- From flamingo dance to (desirable) drug discovery: a nature-inspired approach.Drug discovery today · 2017Review
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 at 5 institutions in 4 countries.
Funding
Abstract
Current advances in systems biology suggest a new change of paradigm reinforcing the holistic nature of the drug discovery process. According to the principles of systems biology, a simple drug perturbing a network of targets can trigger complex reactions. Therefore, it is possible to connect initial events with final outcomes and consequently prioritize those events, leading to a desired effect. Here, we introduce a new concept, 'Systemic Chemogenomics/Quantitative Structure-Activity Relationship (QSAR)'. To elaborate on the concept, relevant information surrounding it is addressed. The concept is challenged by implementing a systemic QSAR approach for phenotypic virtual screening (VS) of candidate ligands acting as neuroprotective agents in Parkinson's disease (PD). The results support the suitability of the approach for the phenotypic prioritization of drug candidates.
Indexed as
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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.