ReviewBriefings in bioinformatics2020
Computational/in silico methods in drug target and lead prediction.
Review in Briefings in bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 124 papers, 2 of them syntheses 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
124 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Exploring the potential of computer simulation models in drug testing and biomedical research: a systematic review.Frontiers in pharmacology · 2025Pooled it
- Meta-analysis ofFrontiers in cellular and infection microbiology · 2025Pooled it
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- From probe to prodrug: immunoproteasome activity as a trigger for disease-selective therapeutics.Signal transduction and targeted therapy · 2026Article
- Machine Learning and Molecular Modeling Strategy for the Identification of CNS-Active Acetylcholinesterase Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Ligand-agnostic off-target site prediction for early toxicity screening by leveraging point cloud-based protein cavity analysis.Briefings in bioinformatics · 2026Article
- FlowDock: A unified flow-based framework for flexible protein-ligand docking and binding affinity prediction.Acta pharmaceutica Sinica. B · 2026Article
- Article
- Natural Products in Epilepsy Treatment: From Traditional Medicine Towards Computational Drug Discovery.Current issues in molecular biology · 2026Review
- Synthesis, characterization, antioxidant and anticancer potential of Kalanchoe pinnata green synthesized silver nanoparticles targeting p53/MDM2 nexus in hepatic cancer: integrated in vitro and in silico study.Scientific reports · 2026Article
- Sensing Compound Substructures Combined with Molecular Fingerprinting to Predict Drug-Target Interactions.Interdisciplinary sciences, computational life sciences · 2026Article
- Harnessing AI to fuse phenotypic signatures for drug target identification: progress in computational modeling.Briefings in bioinformatics · 2026Review
- Synthesis, Computational Analysis, and Pharmacological Evaluation of Novel Schiff Base Hybrids for Anxiolytic and Skeletal Muscle-relaxant Activities.Central nervous system agents in medicinal chemistry · 2026Article
- Quinoline Heterocyclic Clubbed Hydrazone Derivatives as Potential Inhibitors of MutantCurrent medicinal chemistry · 2026Article
- Article
- Mechanism-aware and safety-validated AI for drug repurposing: a critical review and translational pharmacology framework.Frontiers in pharmacology · 2026Review
- The WHO priority list of antibiotic-resistant bacteria: challenges and opportunities for next-generation antimicrobial development.Frontiers in pharmacology · 2026Review
- Phytochemicals from Astragalus zederbaueri as Acetylcholinesterase Inhibitors for Alzheimer's Therapy.PloS one · 2026Article
- Article
- Biotin-Linked Ursolic Acid Conjugates as Selective Anticancer Agents and Target-Identification Tools for Cancer Therapy.Molecules (Basel, Switzerland) · 2025Article
64 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
7 authors.
Funding
Abstract
Drug-like compounds are most of the time denied approval and use owing to the unexpected clinical side effects and cross-reactivity observed during clinical trials. These unexpected outcomes resulting in significant increase in attrition rate centralizes on the selected drug targets. These targets may be disease candidate proteins or genes, biological pathways, disease-associated microRNAs, disease-related biomarkers, abnormal molecular phenotypes, crucial nodes of biological network or molecular functions. This is generally linked to several factors, including incomplete knowledge on the drug targets and unpredicted pharmacokinetic expressions upon target interaction or off-target effects. A method used to identify targets, especially for polygenic diseases, is essential and constitutes a major bottleneck in drug development with the fundamental stage being the identification and validation of drug targets of interest for further downstream processes. Thus, various computational methods have been developed to complement experimental approaches in drug discovery. Here, we present an overview of various computational methods and tools applied in predicting or validating drug targets and drug-like molecules. We provide an overview on their advantages and compare these methods to identify effective methods which likely lead to optimal results. We also explore major sources of drug failure considering the challenges and opportunities involved. This review might guide researchers on selecting the most efficient approach or technique during the computational drug discovery process.
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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.