ReviewCurrent neuropharmacology2026
The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.
Review in Current neuropharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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.
Funding
Abstract
introductionIn the medicinal chemistry (MC) field, artificial intelligence (AI) has been used to establish quantitative structure-activity relationship (QSAR) classification models, virtual screening, drug discovery, drug design, and so on. In this investigation, MC AI studies (AI-MC) (from 2001-2023) underwent quantitative and qualitative modeling analyses.
methodsUsing a hybrid research strategy incorporating content analyses and bibliometric methods, we retrospectively analysed the AI-MC literature using a bibliometrix package (R software) combined with CiteSpace V and VOSviewer programs.
resultsBetween 2001 and 2023, AI-MC articles were published in 92 countries or regions, with China and the United States leading in the number of publications. Also, 196 affiliations were added to AI-MC research; the CHINESE ACADEMY OF SCIENCES contributed the most. Reference clusters were categorized as follows: (1) QSAR, (2) virtual screening, (3) drug discovery, (4) drug design. Predictive model (2020-2021), molecular fingerprints (2021-2023) and scoring function (2021-2023) reflected research frontier keywords. As we look to the future, the ongoing progress and innovation in technology herald the promising development of multimodal and large language models (LLMs) within the realm of MC. DISCUSSION: The integration of AI into MC has significantly transformed the landscape of drug development. AI techniques, particularly machine learning, and deep learning algorithms, have demonstrated remarkable potential in accelerating the discovery and optimization of new drugs. By leveraging large datasets and advanced computational models, AI enhances the efficiency of virtual screening, improves the accuracy of QSAR models, and facilitates the design of novel therapeutic agents. As the technology continues to advance, the development of multimodal and large language models (LLMs) is expected to further revolutionize this field, offering new opportunities for more precise and efficient drug design and discovery.
conclusionWe comprehensively characterized the AI-MC field and determined future trends and hotspots. Importantly, we provided a dynamic oversight of the AI-MC literature and identified key upcoming research areas.
Indexed as
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What Socratic 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 Socratic graph.