ReviewMolecules (Basel, Switzerland)2018
Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery.
Review in Molecules (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 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
46 citing papers in PubMed.
- Elucidation of DNA Primase as a Drug Target in Leishmania donovani: Structure-Guided Inhibitor Identification and Biochemical Validation.Chemical biology & drug design · 2026Article
- Article
- Structure-Based Virtual Screening of New Skp1 Inhibitor Chemotypes Targeting F-Box Binding Interface.Drug design, development and therapy · 2026Article
- The changing landscape of medicinal chemistry optimization.Nature reviews. Drug discovery · 2025Review
- Sequence-Based Protein-Protein Interaction Prediction and Its Applications in Drug Discovery.Cells · 2025Review
- Applications of artificial intelligence in drug discovery for neurological diseases.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- PHACE: Phylogeny-Aware Detection of Molecular Coevolution.Molecular biology and evolution · 2025Article
- Screening Enamine Fragments Library in the Quest for Novel SOS2 Inhibitors: Pharmacophore Modelling, Molecular Docking, MMGBSA Calculations, and MD Simulation.Journal of pharmacy & bioallied sciences · 2025Article
- In Silico Identification of Potential Antagonists Targeting the HPV16 E2-E1 Interaction: A Step Toward Novel Therapeutics for Cervical Cancer.Current issues in molecular biology · 2025Article
- Molecular analysis of HPV16 and HPV18 oncogenes in oral squamous cell carcinoma: Structural, transcriptomic andOncology letters · 2025Article
- Article
- A Survey of Deep Learning Methods for Estimating the Accuracy of Protein Quaternary Structure Models.Biomolecules · 2024Review
- HormoNet: a deep learning approach for hormone-drug interaction prediction.BMC bioinformatics · 2024Article
- Integrated virtual screening, molecular modeling and machine learning approaches revealed potential natural inhibitors for epilepsy.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2023Article
- Designing Cyclic-Constrained Peptides to Inhibit Human Phosphoglycerate Dehydrogenase.Molecules (Basel, Switzerland) · 2023Article
- A robust protein language model for SARS-CoV-2 protein-protein interaction network prediction.Artificial intelligence in medicine · 2023Article
- A gated graph transformer for protein complex structure quality assessment and its performance in CASP15.Bioinformatics (Oxford, England) · 2023Article
- Targeting Protein-Protein Interfaces with Peptides: The Contribution of Chemical Combinatorial Peptide Library Approaches.International journal of molecular sciences · 2023Review
- Computational approaches for the design of modulators targeting protein-protein interactions.Expert opinion on drug discovery · 2023Review
- Unveiling New Druggable Pockets in Influenza Non-Structural Protein 1: NS1-Host Interactions as Antiviral Targets for Flu.International journal of molecular sciences · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.
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.