ArticleJournal of computer-aided molecular design2017
Computer-aided drug design at Boehringer Ingelheim.
Article in Journal of computer-aided molecular design, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed, 66 citations in OpenAlex.
- Evaluating generalization in protein-ligand cofolding methods.Nature structural & molecular biology · 2026Article
- Strategic Key Performance Indicators for AI in Lead Optimization.ChemMedChem · 2026Article
- Virtual Screening of FDA-Approved Compounds: Exploring New Alternatives for HIV Treatment.ACS omega · 2026Article
- Differentiation of RNA-protein docking structures through molecular dynamics simulation and machine learning methods.Briefings in bioinformatics · 2026Article
- A comprehensive review on plant-derived bioactive saponins as promising antimicrobial agents: from bioavailability challenges, molecular mechanistic insights to therapeutic applications.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- Development and Prospects of Furin Inhibitors for Therapeutic Applications.International journal of molecular sciences · 2024Review
- DockOpt: A Tool for Automatic Optimization of Docking Models.Journal of chemical information and modeling · 2024Article
- Predictive Models Based on Molecular Images and Molecular Descriptors for Drug Screening.ACS omega · 2023Article
- Exploring the Molecular Targets for the Antidepressant and Antisuicidal Effects of Ketamine Enantiomers by Using Network Pharmacology and Molecular Docking.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods.ChemMedChem · 2023Article
- Hydrogen bonding penalty used for virtual screening to discover potent inhibitors for Papain-Like cysteine proteases of SARS-CoV-2.Chemical biology & drug design · 2022Article
- Plant Metabolites as SARS-CoV-2 Inhibitors Candidates: In Silico and In Vitro Studies.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Contemporary Computational Applications and Tools in Drug Discovery.ACS medicinal chemistry letters · 2022Article
- Revisiting the therapeutic potential of tocotrienol.BioFactors (Oxford, England) · 2022Review
- Novel QSAR Approach for a Regression Model of Clearance That Combines DeepSnap-Deep Learning and Conventional Machine Learning.ACS omega · 2022Article
- Article
- Therapeutic Potential of Curcumin as an Antimycobacterial Agent.Biomolecules · 2021Review
- Discovery of Hydroxyamidine Derivatives as Highly Potent, Selective Indoleamine-2,3-dioxygenase 1 Inhibitors.ACS medicinal chemistry letters · 2021Article
- Mapping the intellectual structure of the coronavirus field (2000-2020): a co-word analysis.Scientometrics · 2021Article
- SeamDock: An Interactive and Collaborative Online Docking Resource to Assist Small Compound Molecular Docking.Frontiers in molecular biosciences · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 3 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-Aided Drug Design (CADD) is an integral part of the drug discovery endeavor at Boehringer Ingelheim (BI). CADD contributes to the evaluation of new therapeutic concepts, identifies small molecule starting points for drug discovery, and develops strategies for optimizing hit and lead compounds. The CADD scientists at BI benefit from the global use and development of both software platforms and computational services. A number of computational techniques developed in-house have significantly changed the way early drug discovery is carried out at BI. In particular, virtual screening in vast chemical spaces, which can be accessed by combinatorial chemistry, has added a new option for the identification of hits in many projects. Recently, a new framework has been implemented allowing fast, interactive predictions of relevant on and off target endpoints and other optimization parameters. In addition to the introduction of this new framework at BI, CADD has been focusing on the enablement of medicinal chemists to independently perform an increasing amount of molecular modeling and design work. This is made possible through the deployment of MOE as a global modeling platform, allowing computational and medicinal chemists to freely share ideas and modeling results. Furthermore, a central communication layer called the computational chemistry framework provides broad access to predictive models and other computational services.
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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.