ReviewPharmacological reviews2014
Computational methods in drug discovery.
Review in Pharmacological reviews, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 705 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
705 citing papers in PubMed.
- Computational identification of potential inhibitors for chikungunya virus nsP2pro.Royal Society open science · 2026Article
- Bio-active designed peptide for hair keratin strengthening.International journal of cosmetic science · 2026Article
- Antinociceptive and Anti-Inflammatory Effects of Syringic Acid:ACS omega · 2026Article
- Integration of network pharmacology, structure-based pharmacophore modeling, DFT, molecular docking, and molecular dynamics simulations to identify multi-target small molecule inhibitors against Alzheimer's disease.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Virtual Screening and Molecular Dynamics Simulations Identifying Natural Product-Derived Cathepsin K Inhibitors as Potential Therapeutics for Osteoporosis.International journal of molecular sciences · 2026Article
- Structure-Based Virtual Screening andACS omega · 2026Article
- Discovery of Quercetin as a Potential Entry Inhibitor of Nipah Virus: A Path Toward Antiviral Therapy.International journal of molecular sciences · 2026Article
- Chemical Characterization and Pharmacological Activity of Byrsonima crassifolia (L.) Kunth (Murici): Antinociceptive Effects, Edema Reduction, and Molecular Docking.Chemistry & biodiversity · 2026Article
- Review
- Beyond the flat screen: evaluating virtual reality as a complementary tool for molecular docking in drug discovery.Journal of computer-aided molecular design · 2026Review
- Computational applications in secondary metabolite discovery (CAiSMD) 2026: focus on secondary metabolites towards drug discovery.Journal of cheminformatics · 2026Article
- Computational identification of potential VEGFR2 inhibitors using integrated pharmacophore modeling, QSAR analysis, molecular docking, dynamics simulation, and virtual screening followed by in vitro bioassays.Journal of computer-aided molecular design · 2026Article
- GAMT-GINE: A Graph Isomorphism Network Integrating Continuous Spatial Awareness and Multi-Task Learning for Protein-Ligand Binding Affinity Prediction.International journal of molecular sciences · 2026Article
- Ganoderic acid D as a potential modulator of steroidogenic enzymes: Protective effects of Cucurbita maxima Duch. seed extract against nonylphenol-induced reproductive toxicity.Journal of computer-aided molecular design · 2026Article
- Computational Investigation of Cinnamon Phytochemicals Targeting Key Cancer Signaling Pathways: Molecular Docking, ADMET and Molecular Dynamics Simulations Analysis.Current issues in molecular biology · 2026Article
- PyMolGen: Database-Driven Molecular Generation of Drug-Like Compounds.Journal of chemical information and modeling · 2026Article
- Rapid and energy-efficient ultra-large library screening for drug discovery on a SpiNNaker2 neuromorphic chip.Communications chemistry · 2026Article
- Biological activity assessment of isolated flavonoid from Cordia sinensis: in vitro and in silico evaluation against idiopathic pulmonary fibrosis.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- 3DICE: interpretable 3D cross-modal learning for drug-target interaction prediction and large-scale drug discovery.Bioinformatics (Oxford, England) · 2026Article
- Zebrafish swimming towards cures: a scalable NAM platform for drug discovery.Drug discovery today · 2026Review
645 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Computer-aided drug discovery/design methods have played a major role in the development of therapeutically important small molecules for over three decades. These methods are broadly classified as either structure-based or ligand-based methods. Structure-based methods are in principle analogous to high-throughput screening in that both target and ligand structure information is imperative. Structure-based approaches include ligand docking, pharmacophore, and ligand design methods. The article discusses theory behind the most important methods and recent successful applications. Ligand-based methods use only ligand information for predicting activity depending on its similarity/dissimilarity to previously known active ligands. We review widely used ligand-based methods such as ligand-based pharmacophores, molecular descriptors, and quantitative structure-activity relationships. In addition, important tools such as target/ligand data bases, homology modeling, ligand fingerprint methods, etc., necessary for successful implementation of various computer-aided drug discovery/design methods in a drug discovery campaign are discussed. Finally, computational methods for toxicity prediction and optimization for favorable physiologic properties are discussed with successful examples from literature.
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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.