Artificial Intelligence - Driven Approaches in Drug Discovery and Development

Abstract

Artificial intelligence (AI) has transformed drug discovery and development by streamlining research processes, reducing development time and costs, and improving the efficiency and success of identifying promising therapeutic candidates. Drug targets can be quickly identified, compound efficacy may be predicted, and drug design can be optimized thanks toartificial intelligence (AI), which assesses massive datasets usingNatural language processing (NLP), machine learning (ML), and deep learning (DL).Through better patient recruitment and data analysis, it refines clinical trial designs and speeds uplead detection using toxicity, potential adverse effects, and pharmacokinetic predictions.The many advantages of AI in drug development are highlighted in this pa per, including increased accuracy, reduced risks, and increased efficiency. Important issues including data quality, model interpretability, and regulatory obstacles are also covered. Improving data will be necessary for future developments in AI - powered d rug discovery.standardization, encouraging openness in the creation of AI models, and bolstering cooperation between pharmaceutical specialists and AI researchers. By tackling these issues, AI has the power to completely transform healthcare by giving pati ents safer, more efficient, and more reasonably priced medications.