Bioinfo Chem
System biology and Infochemistry
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Volume 7 Number 1 2025
REVIEWS (Open Access)
Artificial Intelligence in Drug Discovery: Systematic Review and Meta-Analysis of Predictive Performance, Structural Modeling, and Translational Reliability
Shunqi Liu 1*, Han Qiu 2
Bioinfo Chem 7 (1) 1-8 https://doi.org/10.25163/bioinformatics.7110594
Submitted: 25 October 2025 Revised: 10 December 2025 Accepted: 18 December 2025 Published: 20 December 2025
Abstract
Artificial intelligence (AI) has rapidly transformed drug discovery by accelerating the identification, optimization, and substantiation of therapeutic candidates. Advances in deep learning, protein structure modeling, molecular simulation, and reproductive models have created unprecedented opportunities to decode the complexity of biological systems and design novel compounds with improved safety and efficacy profiles. Despite significant progress, the extent to which AI raises predictive truth, reduces experimental burdens, and amends drug-likeness across diverse therapeutic domains remains incompletely understood. This systematic review and meta-analysis synthesize evidence from studies employing AI-based structural modeling, molecular property forecasting, virtual screening, and de novo drug design. Databases including PubMed, Scopus, Web of Science, and IEEE Xplore were searched for articles published between 2005 and 2024. Eligible studies assessed AI performance in tasks such as protein structure prognostication, drug-target interaction inference, constipation affinity prediction, ADMET modeling, or molecular generation. Meta-uninflected pooling of performance indicators such as AUROC, RMSE, precision, recall, and top-k hit pace was done utilizing random-effects models. Overall, AI methodology significantly outperformed traditional computational approaches, yielding higher prognosticative accuracy, reduced false-positive rates, and improved structural generalization across chemical space. Deep erudition architectures, particularly graph neural networks and transformer-based models, achieve the highest carrying out gains. However, heterogeneity arose from differences in datasets, model training strategies, and the lack of standardized benchmarks. This review highlights the strengths, limitations, and translational potential of AI-driven drug discovery and provides recommendations for improving reproducibility, validation practice sessions, and clinical relevance in future studies.
Keywords: artificial intelligence, drug discovery, deep learning, molecular modeling, virtual screening, ADMET prediction, protein structure, meta-analysis
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