ArticleMethods in molecular biology (Clifton, N.J.)2022
Application of Artificial Intelligence and Machine Learning in Drug Discovery.
Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Integrated computational and experimental analysis explores FOLH1 expression patterns across cancers and nominates melatonin as a potential modulator in prostate cancer models.PLoS computational biology · 2026Article
- The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.Biology · 2026Review
- Harnessing IDO inhibitors to optimize cancer immunotherapy.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- The Potential of Artificial Intelligence in Pharmaceutical Innovation: From Drug Discovery to Clinical Trials.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Comprehensive applications of the artificial intelligence technology in new drug research and development.Health information science and systems · 2024Review
- Machine Learning in Drug Metabolism Study.Current drug metabolism · 2022Article
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 (ML) and Deep Learning (DL) are two subclasses of Artificial Intelligence (AI), that, in this day and age of big data provides significant opportunities to pharmaceutical discovery research and development by translating data to information and ultimately to knowledge. Machine Learning or AI is not really new but over last few years, application of better methods have emerged and they have been successfully applied for drug discovery and development. This chapter would provide an overview of these methods and how they have been applied across various work streams, e.g., generative chemistry, ADMET prediction, retrosynthetic analysis, etc. within drug discovery process. This chapter would also attempt to provide caution and pit falls in utilizing these methods blindly while summarizing challenges and limitations.
Indexed as
Identifiers
34731466What 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.