1. Explore – Foundations of AI in Biotechnology

1.1 What is Biotechnology?

Biotechnology is a field of biology that involves using living systems and organisms to develop or make products, or "any technological application that uses biological systems, living organisms, or derivatives thereof, to make or modify products or processes for specific use" (Convention on Biological Diversity, 1992). From ancient practices like brewing and baking to modern genetic engineering and vaccine production, biotechnology has a long history of harnessing biological processes for human benefit.

1.2 What is Artificial Intelligence (AI)?

Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, and understanding language. A key subset of AI is Machine Learning (ML), where algorithms learn from data without being explicitly programmed. Deep Learning, a more advanced form of ML, uses complex neural networks to process vast amounts of data, mimicking the human brain's structure.

1.3 How AI powers Biotechnology?

The integration of AI and biotechnology, often referred to as AI-powered biotechnology, marks a paradigm shift in life sciences. AI's ability to analyze massive, complex datasets, identify patterns, and make predictions is revolutionizing various biotechnological applications, from genome sequencing to personalized medicine (Asad & Zulfiqar, 2024).

1.4 Is AI in Drug Discovery a Game Changer?

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.