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Application of Artificial Intelligence in Drug Discovery and Pharmaceutical Formulation Development

Year: 2026
Type: article
Abstract: AI is changing pharmaceutical research by allowing for data-driven decision-making in medication discovery and formulation development. Traditional pharmaceutical workflows are frequently constrained by long deadlines, large attrition rates, and substantial experimental trial-and-error, notably during target identification, lead optimization, and dosage-form design. These obstacles contribute to rising costs and delayed access to novel medicines Recent advances in machine learning (ML) and deep learning (DL) have resulted in computational algorithms that can analyze complicated biological, chemical, and formulation datasets with incredible speed and precision. In drug discovery, AI-based models aid in target selection, virtual screening, protein structure prediction, and early evaluation of pharmacokinetic and toxicity profiles, lowering the likelihood of late-stage failures.AI improves rational medication design by combining disparate information and shortens the time it takes to get from laboratory findings to clinical prospects. AI integration has had a substantial impact on formulation development. Predictive modeling can help with solubility estimation, excipient compatibility assessment, and formulation variable optimization, all of which are usually resource-intensive activities. When integrated with Quality by Design (QbD) frameworks, AI allows for systematic examination of important quality features and process factors, resulting in robust and reproducible formulations. Furthermore, AI-driven techniques are increasingly being used to create innovative drug delivery systems (NDDS) such as nanoparticles, liposomes, microneedles, and smart polymers, which improves bioavailability and therapeutic results.
DOI: 10.5281/zenodo.19327168
Language: English
Open Access status: hybrid
OpenAlex ID: W7143415652
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