ReviewJournal of computer-aided molecular design2026
AI-driven drug design: a comprehensive review.
Review in Journal of computer-aided molecular design, 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
9 authors.
Funding
Abstract
The integration of AI into drug design has undergone a transformative evolution, reshaping the landscape of medicinal chemistry. AI's exceptional capabilities in data processing, pattern recognition, and predictive modelling have permeated every stage of the drug development pipeline. To provide a comprehensive overview of the current state, key methodologies, and emerging trends in this rapidly evolving field, this study systematically examines global research achievements in AI-driven drug design and discovery over the past 22 years. Drawing upon the Science Citation Index-Expanded and Social Sciences Citation Index databases, a multi-dimension analysis was conducted on AI-driven drug design spanning from 2004 to 2025. The dataset underwent rigorous cleaning, knowledge discovery, and visualization using the Derwent Data Analyzer. 16,190 publications in total were systematically reviewed. The findings reveal that China, USA, and India are the most prolific contributors to AI-driven drug design research, with USA demonstrating the highest citation volume. The Chinese Academy of Sciences ranked first in both publication output and H-index, while the Harvard University exhibited the highest average citation per publication. Journal of Chemical Information and Modeling emerged as the most productive journal, and "CHEMISTRY, MULTIDISCIPLINARY" was the predominant disciplinary category. Current research focus includes protein structure prediction, deep learning, drug repurposing (or drug repositioning), and artificial neural networks. Additionally, emerging research frontiers such as chemical language models for network-based target screening, AI in clinical trials, predictive toxicology, and adverse drug reaction analysis are anticipated to drive innovation in the coming years.
Indexed as
Identifiers
42640361What 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.