ReviewCurrent topics in medicinal chemistry2025
The Future of Medicine: AI and ML Driven Drug Discovery Advancements.
Review in Current topics in 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.
- Big Data for Drug Discovery: Some Historical Landscape, Considerations, and Applications for a Medicinal Chemist.ACS omega · 2026Review
- Current Trends in Drug Discovery Based on Artificial Intelligence and Computer-aided Drug Design: Part II.Current topics in medicinal chemistry · 2025Article
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
No grant is acknowledged in the PubMed record.
Abstract
The field of drug design has evolved from conventional approaches relying on empirical evidence to advanced approaches such as Computer-Aided Drug Design (CADD). It aids in intricate phases of drug discovery, such as target discovery, lead optimization, and clinical trials, establishing a safe, rapid, and cost-effective system. Structure based drug design (SBDD), Ligand based drug design (LBDD), and Pharmacophore modelling, being the most utilized techniques of CADD, play a major role in establishing the road map necessary for the discovery. Artificial intelligence (AI) and Machine learning (ML) have improved the field with the incorporation of big data and, thereby, enhancing the efficacy and accuracy of the CADD. Deep Learning (DL), a part of AI helps in processing complex and non-linear data and thereby decreases complexity, increases resource utilization and enhances drug-target interaction prediction. These approaches have revolutionized healthcare by enhancing diagnostic precision and predicting the behavior of drugs. Currently, AI/ML approach has become crucial for rapidly discovering novel insights and transforming healthcare areas lie diagnostics, clinical research, and critical care. In the case of the drug development area, techniques like PBPK modeling and advanced nano-QSAR enhance drug behavior understanding and predict nano material toxicity if any, leading to safe and effective therapeutic predictions and interventions. The advancement of AI/ML techniques will bring accuracy, efficacy, and more patient-tailored responses to the drug development field.
Indexed as
Identifiers
40207759What 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.