Drug discovery remains costly and time-consuming; most of the costs could be explained by the high expenditure on the chemical space and the high rates of attrition observed during the later phases of development. AI has emerged as a potential solution to these issues that support the use of data to predict and create prospective candidates molecular entities. Recent advances in the pharmaceutical sector to date in the area of AI usage are critically reviewed in this review, as well as the conceptual backbone of prediction of protein structure, prediction of drug-likeness and ADMET, and de novo molecular design that are summarized within a unified framework of early-stage drug discovery. One of the literature review types that were undertaken was a narrative inquiry of peer-reviewed publications dated 2018 to 2025, based on major scientific databases. The results prove that AI-based approaches outperform or refine protein structure prediction, improve molecular property prediction, and provide easy passage of construction of chemical space. Moreover, it can be reported that integrative predictive-generative pipelines may reduce a false-positive rate and enhance selection of candidates at the early stage. In general, the further incorporation of AI methods with experimental validation is expected to put computational methods into an even greater perspective and speed up the curve of drug discoveries.

This study presents an integrated artificial intelligence framework for molecular sciences that encompasses structure prediction, drug-likeness and ADMET modeling, and molecular generation. The proposed approach offers a scalable and cost-effective strategy for early-stage drug discovery and holds considerable potential to improve the efficiency of translational research.

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