TL;DR
Current databases and machine learning tools used for resistance prediction from genomic data are analyzed and the potential of the emerging direction of physics-informed artificial intelligence models for enhancing the interpretability of predictions is noted.
This review discusses how AI can accelerate computational drug discovery, virtual screening (VS), and the repurposing of already-approved medications, and it can also optimize combination therapies through computational simulation.
A paradigm shift toward autonomous scientific agents capable of causal reasoning and end-to-end experimental guidance is highlighted, and persistent challenges are discussed, including data bias, limited interpretability, and in silico-to-wet lab translation.
This evidence-based literature review critically examines the evolution and application of computational technologies across the pharmaceutical pipeline, ranging from early expert systems like DENDRAL, computer-aided drug design (CADD), and quantitative structure-activity relationship (QSAR) modeling to AlphaFold 3 bio...
An AI-based framework for predicting drug-likeness using molecular descriptors derived from chemical structures is proposed that integrates data preprocessing, feature selection, machine learning model development, and performance evaluation using metrics such as accuracy, precision, recall, F1-score, and AUROC.
This review systematically summarizes the latest research progress in druggable protein prediction algorithms, providing a comprehensive analysis across three dimensions: data resources, feature engineering, and predictive models.
The results showed the utility of the combination of AI with pathway-level bioinformatics analysis to not only provide predictive accuracy but also provide biological interpretability in the field of precision medicine.
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