ReviewBeilstein journal of organic chemistry2016
Computational methods in drug discovery.
Review in Beilstein journal of organic chemistry, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 190 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
190 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Insights into the computer-aided drug design and discovery based on anthraquinone scaffold for cancer treatment: A systematic review.PloS one · 2024Pooled it
- Targeting ROCK Isoforms by Natural Phytocompounds: An In-Silico Approach to Modulate Mitochondrial Fission and Mitophagy in Neurological Diseases.Neuroinformatics · 2026Article
- Artificial intelligence-assisted lead optimization in drug discovery: bridging computational advances and translational challenges.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026Review
- Unveiling Novel Metalloprotease Inhibitors Targeting Botulinum Neurotoxin Through Structure-Based Virtual Screening of Drug Libraries and Molecular Dynamics Simulations.International journal of molecular sciences · 2026Article
- A Computational Framework for the Design and Development of Isoform Selective PI3Kα Inhibitors as Novel Anticancer Agents.Molecules (Basel, Switzerland) · 2026Article
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- Explainable Artificial Intelligence (XAI) and Molecular Modeling Techniques to Discover Putative HER2 Inhibitors.International journal of molecular sciences · 2026Article
- Integrated Phytochemical and Pharmacological Insights intoPlants (Basel, Switzerland) · 2026Review
- Advancing Antiviral Design: Integrating Natural Products, Computation and Targeted Delivery.Chemical biology & drug design · 2026Review
- Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling.Journal of chemical theory and computation · 2026Article
- Dual Activity Microbial Peptides Catalog.Scientific data · 2026Article
- Disentangled diffusion model for 3D molecular generation with protein-ligand interaction priors.Bioinformatics (Oxford, England) · 2026Article
- Computational screening of natural compounds from traditional Persian medicinal plant for the finding of potential antidepressant.Scientific reports · 2026Article
- Integrative In Silico Identification of TP53-Associated Drug Repurposing Candidates in Lung Adenocarcinoma.Pharmaceuticals (Basel, Switzerland) · 2026Article
- CDK4/6 Inhibitors for Breast Cancer Therapy-A Review of Clinical Trials, Structural and Computational Approaches.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Improving Protein Structure Prediction Using Integrative Cryo-EM and Ion Mobility Mass Spectrometry Modeling.bioRxiv : the preprint server for biology · 2026Article
- Assessment of antiemetic potential of schaftoside through modulation of dopaminergic, serotonergic and muscarinic signaling pathways: in vivo and in silico investigations.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Molegro Virtual Docker for Docking Screens.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Deep Learning Pipeline for Accelerating Virtual Screening in Drug Discovery.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Discovery of Potential Antileishmanial Compounds Through Phenotypic Screening of an Alkaloid Library.Molecules (Basel, Switzerland) · 2025Article
130 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
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The process for drug discovery and development is challenging, time consuming and expensive. Computer-aided drug discovery (CADD) tools can act as a virtual shortcut, assisting in the expedition of this long process and potentially reducing the cost of research and development. Today CADD has become an effective and indispensable tool in therapeutic development. The human genome project has made available a substantial amount of sequence data that can be used in various drug discovery projects. Additionally, increasing knowledge of biological structures, as well as increasing computer power have made it possible to use computational methods effectively in various phases of the drug discovery and development pipeline. The importance of in silico tools is greater than ever before and has advanced pharmaceutical research. Here we present an overview of computational methods used in different facets of drug discovery and highlight some of the recent successes. In this review, both structure-based and ligand-based drug discovery methods are discussed. Advances in virtual high-throughput screening, protein structure prediction methods, protein-ligand docking, pharmacophore modeling and QSAR techniques are reviewed.
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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.