ArticleDrug discovery today. Technologies2019
The good, the bad, and the ugly in chemical and biological data for machine learning.
Article in Drug discovery today. Technologies, 2019. 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.
- Artificial intelligence-driven discovery of coumarin-based therapeutics: Revolutionizing target identification and validation.Pharmaceutical science advances · 2026Review
- A Combined Chemoinformatics- and Machine Learning-Based Approach Identifies Chlormidazole as a Drug Repurposing Candidate against Aggressive Prostate Cancer.Journal of medicinal chemistry · 2026Article
- Artificial Intelligence Predictions in Huge Chemical Spaces: Chiroptical Properties of [6]-helicene Family.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Assessing biological activities of polyphenols in the context of 4R - challenges of a ubiquitous compound class with complex modes of action.Frontiers in pharmacology · 2026Review
- Adaptive sampling methods facilitate the determination of reliable dataset sizes for evidence-based modeling.Frontiers in bioinformatics · 2025Article
- Article
- Potential anti-obesity effect of saponin metabolites from adzuki beans: A computational approach.Food science & nutrition · 2024Article
- Navigating the archaeal frontier: insights and projections from bioinformatic pipelines.Frontiers in microbiology · 2024Article
- Graph Neural Networks and Structural Information on Ionic Liquids: A Cheminformatics Study on Molecular Physicochemical Property Prediction.The journal of physical chemistry. B · 2023Article
- Limitations of representation learning in small molecule property prediction.Nature communications · 2023Article
- The rise of automated curiosity-driven discoveries in chemistry.Chemical science · 2023Review
- Artificial Intelligence's Impact on Drug Discovery and Development From Bench to Bedside.Cureus · 2023Review
- A focus on the use of real-world datasets for yield prediction.Chemical science · 2023Article
- Explainable uncertainty quantifications for deep learning-based molecular property prediction.Journal of cheminformatics · 2023Article
- Automatic acoustic recognition of pollinating bee species can be highly improved by Deep Learning models accompanied by pre-training and strong data augmentation.Frontiers in plant science · 2023Article
- A semi-automated material exploration scheme to predict the solubilities of tetraphenylporphyrin derivatives.Communications chemistry · 2022Article
- Evaluation guidelines for machine learning tools in the chemical sciences.Nature reviews. Chemistry · 2022Review
- Modulating Target Protein Biology Through the Re-mapping of Conformational Distributions Using Small Molecules.Frontiers in chemistry · 2021Article
- Problems that Can Occur when Assaying Extracts to Pure Compounds in Biological Systems.Current therapeutic research, clinical and experimental · 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning and artificial intelligence (ML/AI) have become important research tools in molecular medicine and chemistry. Their rise and recent success in drug discovery promises a rapid progression of development pipelines while reshaping how fundamental and clinical research is conducted. By taking advantage of the ever-growing wealth of publicly available and proprietary data, learning algorithms now provide an attractive means to generate statistically motivated research hypotheses. Hitherto unknown data patterns may guide and prioritize experiments, and augment expert intuition. Therefore, data is a key component in the model building workflow. Herein, I aim to discuss types of chemical and biological data according to their quality and reemphasize general recommendations for their use in ML/AI.
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.