ArticleJournal of cheminformatics2015
When drug discovery meets web search: Learning to Rank for ligand-based virtual screening.
Article in Journal of cheminformatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Machine learning-based QSAR and molecular modeling identify promising PTP1B modulators from Ocimum gratissimum for type 2 diabetes therapy.Molecular diversity · 2026Article
- Article
- Machine Learning-Driven Bioprospecting of Cholinergic Medicinal Plants for Putative Leads Against Butyrylcholinesterase Towards Alzheimer's Care.Biochemistry research international · 2026Article
- In silico methods for drug-target interaction prediction.Cell reports methods · 2025Review
- RankMHC: Learning to Rank Class-I Peptide-MHC Structural Models.Journal of chemical information and modeling · 2024Article
- Virtual Screening-Based Drug Development for the Treatment of Nervous System Diseases.Current neuropharmacology · 2023Review
- Ranking-Oriented Quantitative Structure-Activity Relationship Modeling Combined with Assay-Wise Data Integration.ACS omega · 2021Article
- Drug-Target Interaction Prediction Based on Adversarial Bayesian Personalized Ranking.BioMed research international · 2021Article
- Application of Machine Learning for Drug-Target Interaction Prediction.Frontiers in genetics · 2021Review
- Cognitive biomarker prioritization in Alzheimer's Disease using brain morphometric data.BMC medical informatics and decision making · 2020Article
- How Sure Can We Be about ML Methods-Based Evaluation of Compound Activity: Incorporation of Information about Prediction Uncertainty Using Deep Learning Techniques.Molecules (Basel, Switzerland) · 2020Article
- QEX: target-specific druglikeness filter enhances ligand-based virtual screening.Molecular diversity · 2019Article
- Prediction Methods of Herbal Compounds in Chinese Medicinal Herbs.Molecules (Basel, Switzerland) · 2018Review
- DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank.Bioinformatics (Oxford, England) · 2016Article
- Opportunities for Web-based Drug Repositioning: Searching for Potential Antihypertensive Agents with Hypotension Adverse Events.Journal of medical Internet research · 2016Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe rapid increase in the emergence of novel chemical substances presents a substantial demands for more sophisticated computational methodologies for drug discovery. In this study, the idea of Learning to Rank in web search was presented in drug virtual screening, which has the following unique capabilities of 1). Applicable of identifying compounds on novel targets when there is not enough training data available for these targets, and 2). Integration of heterogeneous data when compound affinities are measured in different platforms.
resultsA standard pipeline was designed to carry out Learning to Rank in virtual screening. Six Learning to Rank algorithms were investigated based on two public datasets collected from Binding Database and the newly-published Community Structure-Activity Resource benchmark dataset. The results have demonstrated that Learning to rank is an efficient computational strategy for drug virtual screening, particularly due to its novel use in cross-target virtual screening and heterogeneous data integration.
conclusionsTo the best of our knowledge, we have introduced here the first application of Learning to Rank in virtual screening. The experiment workflow and algorithm assessment designed in this study will provide a standard protocol for other similar studies. All the datasets as well as the implementations of Learning to Rank algorithms are available at http://www.tongji.edu.cn/~qiliu/lor_vs.html. Graphical AbstractThe analogy between web search and ligand-based drug discovery.
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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.