Introduction
The development of biological drugs — such as antibodies, therapeutic proteins, and vaccines — is a complex and lengthy process that typically takes 10 to 15 years and costs billions of dollars. In recent years, artificial intelligence (AI) has revolutionized this field by accelerating research steps, improving design accuracy, and reducing costs.

What Are Biological Drugs?
Biological drugs are derived from living organisms or biological components and are used to treat complex diseases such as cancer, autoimmune disorders, and genetic conditions. Among the most well-known biological drugs are:

Monoclonal antibodies

Genetically engineered proteins

Smart vaccines

Gene and cell therapies

How Is AI Used in Their Development?
Target Identification
AI is employed to analyze vast genomic and proteomic databases to identify genes or receptors that can serve as effective drug targets.

Protein and Antibody Design
Deep learning technologies, such as AlphaFold by DeepMind, have enabled highly accurate predictions of the three-dimensional structure of proteins, greatly facilitating the design of specialized biological drugs.

Drug-Body Interaction Analysis
AI models predict drug efficacy, toxicity, and immune response before lab testing, helping reduce the number of failed experiments.

Accelerating Clinical Trials
By analyzing patient data and identifying patterns, AI can select the most suitable participants for clinical trials and predict individual responses to treatments.

Production and Genetic Modification
AI helps optimize biological production lines (e.g., CHO cells used in protein production), making manufacturing more efficient and less costly.

Practical Examples
BioNTech used AI technologies to design an mRNA vaccine against COVID-19 in record time.

AbCellera leverages AI to discover new antibodies for treating cancer and rare diseases.

Insilico Medicine developed candidate drug molecules using AI in just a few days.

Challenges and Limitations
Despite these breakthroughs, several challenges remain:

Difficulty in interpreting some AI model decisions (Black Box Problem)

The need for large, high-quality datasets for training

Ethical considerations regarding the use of biological data

Regulatory frameworks lagging behind technological advancements

Conclusion
AI has become an essential tool in the development of biological drugs, contributing to faster innovation, reduced costs, and improved treatment quality. Although challenges persist, the future promises a shift toward a new era of precision medicine — thanks to the fusion of biology and artificial intelligence.

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