ReviewRSC medicinal chemistry2025
Bridging traditional and contemporary approaches in computational medicinal chemistry: opportunities for innovation in drug discovery.
Review in RSC medicinal chemistry, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Beyond the Score: Fixed-Budget Benchmarking of Virtual Screening Integration Strategies for Decision-Centric Drug Discovery.International journal of molecular sciences · 2026Article
- New Personalized Medicine Model for Medication Management.Journal of personalized medicine · 2026Review
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
The field of computational medicinal chemistry has undergone significant advancements, transitioning from traditional methodologies to contemporary strategies powered by artificial intelligence, machine learning, and big data. Traditional approaches, such as molecular docking and QSAR modeling, have long been the foundation of drug discovery, offering reliable frameworks for target identification and lead optimization. However, contemporary methodologies, including AI-driven target identification, adaptive virtual screening, and generative models, are reshaping the landscape by increasing efficiency and expanding chemical space exploration. This article provides a comprehensive comparison between these two paradigms, highlighting their respective strengths, limitations, and the potential of their integration. By bridging traditional and contemporary approaches, researchers can establish innovative workflows to accelerate drug discovery, ultimately contributing to the development of safer and more effective therapeutics.
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.