Artificial intelligence as a tool in drug discovery and development

Table 1 Applications of artificial intelligence in drug discovery and development

Application area	AI techniques used	Key benefits	Challenges
Target identification	Machine learning, deep learning	Identifying novel drug targets, high accuracy	Data quality, complexity of biological systems
Drug screening	Virtual screening, predictive models	Faster screening of compounds, cost-effective	False positives/negatives, model validation
Lead optimization	QSAR models, reinforcement learning	Improved candidate selection, reduced development time	Integration with traditional methods, data scarcity
Preclinical development	Image analysis, natural language processing	Enhanced understanding of drug toxicity and efficacy	Interpretation of complex data, standardization
Clinical trials	Predictive analytics, patient recruitment algorithms	Optimized trial design, better patient stratification	Ethical concerns, data privacy
Personalized medicine	Genomic data analysis, personalized algorithms	Tailored treatments, improved patient outcomes	Data integration, regulatory issues

AI: Artificial intelligence; QSAR: Quantitative structure-activity relationship.