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Bioinformatics & AI with Python Programming

Master Genomic Data Science, Protein Folding with AlphaFold, and AI-Driven Drug Discovery

DNA is the ultimate code. Learn how to program it. Bridge the gap between classic bioinformatics and the AI revolution. From DNA sequence manipulation with Biopython to predicting protein structures with AlphaFold and building autonomous RAG agents for drug discovery, this volume is your roadmap to the future of life sciences. Don’t just process data—engineer the future of medicine with Python.

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About the Book

Unlock the Power of Life Sciences with Python and Artificial Intelligence.

In the era of the genomic revolution, the ability to process biological data is no longer just a skill for biologists—it is a mandatory toolkit for the modern data scientist and software engineer. Bioinformatics & AI with Python Programming (Volume 13 of the series) provides a comprehensive, hands-on journey from the fundamental strings of DNA to the cutting-edge AI models that are reinventing drug discovery.

This volume bridges the gap between classic bioinformatics and modern Deep Learning. You will start by mastering sequence manipulation and Biopython, then progress to complex structural analysis using AlphaFold and ESMFold outputs. Finally, you will build autonomous AI agents capable of mining PubMed to synthesize novel scientific hypotheses.

What’s Inside:

  • Genomic Foundations: Manipulate DNA, RNA, and Protein sequences using pure Python and Biopython.
  • Database Mining: Automate complex queries to NCBI Entrez and handle high-throughput BLAST searches.
  • Structural Biology: Parse PDB files, calculate molecular geometry, and interpret AlphaFold 2 / ESMFold outputs.
  • Computer-Aided Drug Discovery: Use RDKit to generate molecular fingerprints and perform virtual screening for new lead compounds.
  • Biological NLP: Fine-tune Transformer models like DNABERT for genomic classification tasks.
  • AI Research Agents: Build a RAG (Retrieval-Augmented Generation) system to summarize and analyze scientific papers autonomously.


Every chapter includes theoretical foundations, common pitfalls to avoid, and advanced application scripts designed for real-world pipelines. Whether you are an aspiring bioinformatician or an AI engineer looking to enter the life sciences, this book is your definitive guide to the future of biology.

All the source code is also on GitHub.

Check also the other books in this series

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Author

About the Author

Edgar Milvus

A veteran software engineer with 20 years of experience, I have dedicated my career to the art of automation. My philosophy is simple: programming should eliminate repetitive chores to unlock human creativity. This journey began early on with the development of custom code-generation tools and has evolved into a deep mastery of LLMs and their APIs. Today, I specialize in architecting AI-driven solutions that handle everything from complex coding and security tasks to advanced knowledge retrieval, transforming the way we interact with technology

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