ArticleArtificial intelligence review2023
Navigating with chemometrics and machine learning in chemistry.
Article in Artificial intelligence review, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Review
- Multimodal machine learning for integrating heterogeneous analytical systems.Analytical sciences : the international journal of the Japan Society for Analytical Chemistry · 2026Article
- How Artificial Intelligence Can Advance Electrochemical Science and Identify Water Molecule Orientation on Platinum Electrodes.The journal of physical chemistry. C, Nanomaterials and interfaces · 2026Review
- The Role of Artificial Intelligence in Modern Analytical Chemistry: Current Trends and Future Directions.International journal of analytical chemistry · 2026Review
- Application and Prospects of Large Language Models in Small-Molecule Drug Discovery.Analytical chemistry · 2025Review
- Artificial Intelligence-Based Methods and Omics for Mental Illness Diagnosis: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Refining the rheological characteristics of high drug loading ointment via SDS and machine learning.PloS one · 2024Article
- Medicinal polypharmacology-a scientific glossary of terminology and concepts.Frontiers in pharmacology · 2024Review
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
Chemometrics and machine learning are artificial intelligence-based methods stirring a transformative change in chemistry. Organic synthesis, drug discovery and analytical techniques are incorporating machine learning techniques at an accelerated pace. However, machine-assisted chemistry faces challenges while solving critical problems in chemistry due to complex relationships in data sets. Even with increasing publishing volumes on machine learning, its application in areas of chemistry is not a straightforward endeavour. A particular concern in applying machine learning in chemistry is data availability and reproducibility. The present review article discusses the various chemometric methods, expert systems, and machine learning techniques developed for solving problems of organic synthesis and drug discovery with selected examples. Further, a concise discussion on chemometrics and ML deployed in analytical techniques such as, spectroscopy, microscopy and chromatography are presented. Finally, the review reflects the challenges, opportunities and future perspectives on machine learning and automation in chemistry. The review concludes by pondering on some tough questions on applying machine learning and their possibility of navigation in the different terrains of chemistry.
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.