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Ahmad Guni

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#graph neural networks Review Open access Sep 2026

Predictive accuracy of peptide–target interaction models in drug discovery: a systematic review and meta-analysis

Background Peptides represent promising therapeutic agents due to their high specificity, biocompatibility, and capacity to modulate protein–protein interactions. However, the field faces critical challenges: inconsistent evaluation metrics, heterogeneous datasets, and poor reproducibility, which together undermine objective model comparison and benchmarking. Objectives To systematically review and quantitatively assess the predictive performance of machine learning (ML)-based peptide–target interaction (PTI) models, with emphasis on commonly reported metrics including area under the curve (AUC), concordance index (CI), and Precision. Methods We conducted a systematic literature search across PubMed, arXiv, and Cochrane databases for studies published through 1 August 2025. Inclusion criteria required original ML-based peptide–target prediction models with quantitative performance metrics. Results Twenty-three studies met inclusion criteria. Fourteen reported AUC values (pooled estimate: 0.87, 95% Confidence Interval: 0.83–0.90), five reported Concordance Index values (0.90, 95% Confidence Interval: 0.89–0.91), and six reported precision (0.75, 95% Confidence Interval: 0.69–0.81). Top-performing models predominantly employed transformer or graph neural network architectures with structural input features. Critical limitations included inconsistent reporting practices, infrequent external validation, and limited data/code availability. The observation that the twenty-three studies present challenges to a pooled estimate is itself a consequence of the reporting practices we document, and is why we advance STRIDE. Conclusion ML models demonstrate strong potential for PTI prediction, with leading approaches achieving robust classification and ranking performance. Nevertheless, progress is hindered by non-standardised evaluation metrics, limited transparency, and insufficient reproducibility. We introduce the STRIDE framework (encompassing Standardization, Transparency, Representativeness, Integration, Discovery, and Evidence) to establish rigorous evaluation standards, enhance methodological reproducibility, and support future evaluation of clinical applicability.

William Waldock, Ahmad Guni, Ara Darzi et al. · Frontiers in Drug Discovery · 0 citations

#generative ai Open access Sep 2026

Large language medicine: defining a new paradigm in human health

Artificial intelligence (AI) is poised to revolutionize our understanding of disease and pre-disease, which could transform the way medicine is practiced. Advances in deep learning systems, alongside the advent of generative AI and large language models, promise to usher in an era of multimodal AI systems that permeate every aspect of medicine. AI is expected to support the full spectrum of public health and clinical functions, including surveillance, monitoring, protection, health promotion, and disease prevention. In parallel, molecular biology has been revolutionized by AI solutions that can accurately model proteins and other biological molecules at scale with the potential to accelerate drug discovery and reshape our understanding of disease and pre-disease mechanisms. However, AI has yet to be incorporated into common day-to-day practice, underscoring the difficulty of clinical integration. Achieving this goal will require advancing algorithmic architecture, sourcing higher-quality multimodal data, increasing processing power and efficiency, developing secure and fit-for-purpose data infrastructures, ensuring interoperability with diverse health systems and clinical workflows, and establishing regulatory pathways to protect patient safety and clarify clinician liability. The potential emergence of autonomous systems capable of carrying out an increasing number of clinical tasks raises important ethical and legal questions about accountability and responsibility in patient care. This review explores the current state of AI in medicine, highlighting that current clinically mature and regulatory-approved products are based on deep learning architecture and are predominantly diagnostic. Robust regulatory frameworks and ethical guidelines must be established to govern the development and deployment of AI, ensuring alignment with patient safety, clinical guidelines, and public trust. Multidisciplinary collaboration among clinicians, researchers, technologists, ethicists, regulators, and policymakers is essential moving forward.

Ahmad Guni, Wanheng Hu, Jessica Morley et al. · Frontiers in Science · 1 citation