Artificial Intelligence in Drug Discovery : Current Application and Future Perspectives

Abstract:computational chemistry, drug discovery is shifting from trial-and-error methods to a moresystematic approach .Artificial intelligence (AI), especially machine learning (ML) and deep learning(DL), has transformed drug development by improving data analysis and prediction. Natural products(NPs) have always been a valuable source of medicinal compounds, with many drugs coming fromthem. With advances in information technology, NP-related databases have grown, making it easierto explore them using AI. Using AI in NP drug discovery speeds up the process by analyzing largeamounts of data efficiently. Generative AI can even help create new data. This article looks at how AIis currently used in NP drug discovery, discussing its benefits, challenges, and future possibilities toimprove this important area of research.[1]The use of artificial intelligence (AI) in medicine, especially through machine learning (ML), hasgreatly improved drug discovery. AI helps bridge the gap between understanding diseases andfinding new medicines.This article gives a complete summary of the latest progress in AI and its rolein drug discovery. It looks at different stages of the process, starting from identifying diseases todiagnosis, finding drug targets, screening, and discovering lead compounds. AI can analyze largeamounts of data and recognize patterns, making it useful in these steps. It improves predictions andmakes disease identification, drug discovery, and clinical trials more efficient.AI plays a key role inspeeding up drug development by handling vast data efficiently. This helps reduce the time and costneeded to bring new medicines to market.[2]