ReviewF1000Research2015
Computer-aided drug discovery.
Review in F1000Research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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
14 citing papers in PubMed.
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- V-SYNTHES2-the next generation tool for structure-based virtual screening of giga-scale chemical spaces.npj drug discovery · 2026Article
- V-synthes2 - the Next Generation Tool for Structure-based Virtual Screening of Giga-scale Chemical Spaces.Research square · 2025Article
- Virtual Screening of Novel Benzothiozinone Derivatives to Predict Potential Inhibitors of Mycobacterium Tuberculosis Kinases 2D-QSAR, Molecular Docking, MM-PBSA Dynamics Simulations, and ADMET Properties.International journal of molecular sciences · 2025Article
- Enhancing HCV NS3 Inhibitor Classification with Optimized Molecular Fingerprints Using Random Forest.International journal of molecular sciences · 2025Article
- In silico screening, synthesis, characterization and biological evaluation of novel anticancer agents as potential COX-2 inhibitors.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2023Article
- Rationalizing general limitations in assessing and comparing methods for compound potency prediction.Scientific reports · 2023Article
- Computer Aided Drug Design Methodologies with Natural Products in the Drug Research Against Alzheimer's Disease.Current neuropharmacology · 2022Review
- Structure Activity Relationship of Key Heterocyclic Anti-Angiogenic Leads of Promising Potential in the Fight against Cancer.Molecules (Basel, Switzerland) · 2021Review
- Deep neural network affinity model for BACE inhibitors in D3R Grand Challenge 4.Journal of computer-aided molecular design · 2020Article
- Developing RNA aptamers for potential treatment of neurological diseases.Future medicinal chemistry · 2019Review
- Aromatic Rings Commonly Used in Medicinal Chemistry: Force Fields Comparison and Interactions With Water Toward the Design of New Chemical Entities.Frontiers in pharmacology · 2018Article
- Docking-undocking combination applied to the D3R Grand Challenge 2015.Journal of computer-aided molecular design · 2016Article
- Integrated Approaches to Drug Discovery for Oxidative Stress-Related Retinal Diseases.Oxidative medicine and cellular longevity · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computational approaches are an integral part of interdisciplinary drug discovery research. Understanding the science behind computational tools, their opportunities, and limitations is essential to make a true impact on drug discovery at different levels. If applied in a scientifically meaningful way, computational methods improve the ability to identify and evaluate potential drug molecules, but there remain weaknesses in the methods that preclude naïve applications. Herein, current trends in computer-aided drug discovery are reviewed, and selected computational areas are discussed. Approaches are highlighted that aid in the identification and optimization of new drug candidates. Emphasis is put on the presentation and discussion of computational concepts and methods, rather than case studies or application examples. As such, this contribution aims to provide an overview of the current methodological spectrum of computational drug discovery for a broad audience.
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.