It is concluded that future progress will depend less on increasingly sophisticated algorithms than on trustworthy AI systems that improve scientific decision-making within iterative lead optimization workflows and ultimately enhance translational success in drug discovery.
The analysis suggests that AI will become an increasingly important component of pharmaceutical workflows; however, long-term impact will depend less on algorithmic advancement alone and more on effective integration with biological validation, experimental rigor, clinical evidence, and scalable translational infrastru...
Andrew Matelis· American Journal of Student...· 0 citations
Artificial intelligence (AI) is transforming computer-aided drug design (CADD) by enabling more rapid, efficient, and data-driven approaches to drug discovery. This narrative review examines recent applications of AI, machine learning (ML), deep learning (DL), reinforcement learning (RL), natural language processing (N...
Uma Rawat, Anita Singh· Current Computer - Aided Dru...· 0 citations
This review examines the available literature from human clinical studies, computational drug discovery research, systematic reviews, meta-analyses, and clinical investigations, highlighting the applications of AI in target identification, virtual screening, lead optimization, drug repurposing, ADMET prediction, and pr...
Neha Arora, Yogesh Matta, Monu Kumar et al.· Journal of Pharmaceutical Re...· 0 citations
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...
Leonardo Mairene Muniz· Brazilian Journal of Health...· 0 citations
The evolving role of AI in modern drug discovery is discussed while highlighting the importance of explainable algorithms, high-quality biomedical data, real-world evidence, and interdisciplinary collaboration.
Mohsen Zabihi· Advances in Pharmacology and...· 0 citations
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