ArticleF1000Research2021
Rationality over fashion and hype in drug design.
Article in F1000Research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed, 37 citations in OpenAlex.
- Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data.Journal of cheminformatics · 2023Article
- In-Depth Analysis of Physiologically Based Pharmacokinetic (PBPK) Modeling Utilization in Different Application Fields Using Text Mining Tools.Pharmaceutics · 2022Review
- Critical Review of Plant-Derived Compounds as Possible Inhibitors of SARS-CoV-2 Proteases: A Comparison with Experimentally Validated Molecules.ACS omega · 2022Review
- Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds.Journal of cheminformatics · 2022Article
- Progress on open chemoinformatic tools for expanding and exploring the chemical space.Journal of computer-aided molecular design · 2022Article
- Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry.International journal of molecular sciences · 2022Review
- Natural product drug discovery in the artificial intelligence era.Chemical science · 2022Review
- Theoretical Studies on the Molecular Properties, Toxicity, and Biological Efficacy of 21 New Chemical Entities.ACS omega · 2021Article
- A machine learning framework for predicting drug-drug interactions.Scientific reports · 2021Article
- Cytotoxic and Antifungal Amides Derived from Ferulic Acid: Molecular Docking and Mechanism of Action.BioMed research international · 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
5 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The current hype associated with machine learning and artificial intelligence often confuses scientists and students and may lead to uncritical or inappropriate applications of computational approaches. Even the field of computer-aided drug design (CADD) is not an exception. The situation is ambivalent. On one hand, more scientists are becoming aware of the benefits of learning from available data and are beginning to derive predictive models before designing experiments. However, on the other hand, easy accessibility of
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.