1. Explore – Foundations of AI in Biotechnology

Drug discovery is a prime example of where AI is making a significant impact. Traditionally, this process is resource-intensive, time-consuming, and has a high failure rate. AI is transforming each stage, offering improvements in efficiency, cost reduction, and success rates (Pușcașu et al., 2025). Here’s how AI is accelerating drug discovery:

• Faster Target Identification: AI rapidly analyzes biological data (genomics, proteomics) to quickly pinpoint disease-relevant therapeutic targets, shortening the initial discovery phase.
• Accelerated Virtual Screening: AI-powered computational models rapidly screen billions of chemical compounds virtually, predicting which are likely to bind to a specific disease target. This greatly speeds up identifying potential drug candidates compared to physical testing (Marins et al., 2024).
• Efficient Drug Design: AI, including generative AI, designs novel molecular structures from scratch, optimizing them for desired biological activities and refining existing drug candidates to improve efficacy and safety.
• Improved ADMET Prediction: AI models accurately predict a drug's Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) early on. This allows researchers to quickly filter out harmful or ineffective compounds, preventing costly failures in later stages.
• Optimized Clinical Trials: AI analyzes patient data to efficiently identify suitable trial participants and design more targeted protocols. It also predicts responses and monitors outcomes in real-time, leading to faster recruitment, shorter trial durations, and higher success rates.
• Rapid Drug Repurposing: AI algorithms quickly identify new therapeutic uses for existing drugs by analyzing vast datasets of approved medications and disease information, accelerating treatment availability.