Artificial intelligence (AI) can transform drug discovery and early drug development
by addressing inefficiencies in traditional methods, which often face high costs,
long timelines, and low success rates. In this review we provide an overview of how
to integrate AI to the current drug discovery and development process, as it can enhance
activities like target identification, drug discovery, and early clinical development.
Through multiomics data analysis and network-based approaches, AI can help to identify
novel oncogenic vulnerabilities and key therapeutic targets. AI models, such as AlphaFold,
predict protein structures with high accuracy, aiding druggability assessments and
structure-based drug design. AI also facilitates virtual screening and de novo drug
design, creating optimized molecular structures for specific biological properties.
In early clinical development, AI supports patient recruitment by analyzing electronic
health records and improves trial design through predictive modeling, protocol optimization,
and adaptive strategies. Innovations like synthetic control arms and digital twins
can reduce logistical and ethical challenges by simulating outcomes using real-world
or virtual patient data. Despite these advancements, limitations remain. AI models
may be biased if trained on unrepresentative datasets, and reliance on historical
or synthetic data can lead to overfitting or lack generalizability. Ethical and regulatory
issues, such as data privacy, also challenge the implementation of AI. In conclusion,
in this review we provide a comprehensive overview about how to integrate AI into
current processes. These efforts, although they will demand collaboration between
professionals, and robust data quality, have a transformative potential to accelerate
drug development.
