TL;DR
As a result of the integration of AI, both preclinical and clinical research are undergoing profound transformation, with justified hope that effective and safe therapies can finally be developed more rapidly and cost-efficiently.
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...
The practice of artificial intelligence has recently expanded several social spheres, with the pharmaceutical domain leading the way. Given that AI can facilitate rational drug design, it is plausible to envision its involvement in developing pharmaceutical products from the laboratory to the patient’s bedside. AI reco...
In silico medicine, which depends on computational modeling and simulation, is becoming more popular in drug discovery worldwide. Biomedical research is being rapidly transformed by Artificial Intelligence (AI), but there are risks associated with regulatory ambiguity. The integration of AI offers significant oppor...
The aim was to demonstrate the potential of using artificial intelligence (AI) in pharmacology and the development of new drugs. This study reviews the key technological drivers shaping the market for generative design and AI applications in medicine, the dynamics of the global AI drug discovery market, the activit...
It is essential to view AI technologies as an instrumental but supplementary part of decision-making in pharmaceutical research and development rather than a fully independent alternative to traditional hit- and lead-discovery strategies.
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