ReviewBriefings in bioinformatics2018
Data-driven approaches used for compound library design, hit triage and bioactivity modeling in high-throughput screening.
Review in Briefings in bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Review
- Artificial intelligence-assisted neuropharmacology: reshaping drug discovery and translational strategies for neurological diseases.Turkish journal of medical sciences · 2026Review
- Establishment of High-Throughput Screening Assay using 384-Well Plate for Identification of Potent Antioxidants from Malaysian Local Plants Repository and Phytochemical Profile ofTropical life sciences research · 2025Article
- Artificial Intelligence-Driven Innovations in Oncology Drug Discovery: Transforming Traditional Pipelines and Enhancing Drug Design.Drug design, development and therapy · 2025Review
- Inverse mapping of quantum properties to structures for chemical space of small organic molecules.Nature communications · 2024Article
- Step Forward Cross Validation for Bioactivity Prediction: Out of Distribution Validation in Drug Discovery.bioRxiv : the preprint server for biology · 2024Article
- GLDM: hit molecule generation with constrained graph latent diffusion model.Briefings in bioinformatics · 2024Article
- High-Throughput Screening of Natural Product and Synthetic Molecule Libraries for Antibacterial Drug Discovery.Metabolites · 2023Review
- Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade.Pharmaceuticals (Basel, Switzerland) · 2023Review
- High-Throughput Synthesis of Thin Films for the Discovery of Energy Materials: A Perspective.ACS materials Au · 2022Review
- Structure-Based Discovery of Lipoteichoic Acid Synthase Inhibitors.Journal of chemical information and modeling · 2022Article
- Ultrahigh Throughput Protein-Ligand Docking with Deep Learning.Methods in molecular biology (Clifton, N.J.) · 2022Article
- BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space.International journal of molecular sciences · 2021Article
- Virtual screening web servers: designing chemical probes and drug candidates in the cyberspace.Briefings in bioinformatics · 2021Review
- Prediction of pharmacological activities from chemical structures with graph convolutional neural networks.Scientific reports · 2021Article
- QSAR-derived affinity fingerprints (part 1): fingerprint construction and modeling performance for similarity searching, bioactivity classification and scaffold hopping.Journal of cheminformatics · 2020Article
- De novo generation of hit-like molecules from gene expression signatures using artificial intelligence.Nature communications · 2020Article
- Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning.Chemical science · 2019Article
- VB-MK-LMF: fusion of drugs, targets and interactions using variational Bayesian multiple kernel logistic matrix factorization.BMC bioinformatics · 2017Article
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
High-throughput screening (HTS) campaigns are routinely performed in pharmaceutical companies to explore activity profiles of chemical libraries for the identification of promising candidates for further investigation. With the aim of improving hit rates in these campaigns, data-driven approaches have been used to design relevant compound screening collections, enable effective hit triage and perform activity modeling for compound prioritization. Remarkable progress has been made in the activity modeling area since the recent introduction of large-scale bioactivity-based compound similarity metrics. This is evidenced by increased hit rates in iterative screening strategies and novel insights into compound mode of action obtained through activity modeling. Here, we provide an overview of the developments in data-driven approaches, elaborate on novel activity modeling techniques and screening paradigms explored and outline their significance in HTS.
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.