This appendix serves as a consolidated bibliography for the book’s main chapters, organizing the citations used throughout the text into numbered reference lists. It explains that in-text citations are formatted as bracketed numbers that map to the entries provided here, while noting that some other appendices maintain their own separate references.
Across the chapter references, the book draws on a broad mix of sources that reflect the full AI drug discovery workflow. These include foundational work on proteomics, high-throughput screening, pharmaceutical R&D economics, chemical space, drug-likeness rules, descriptors, fingerprints, scaffold analysis, and virtual screening, along with practical resources from software ecosystems and technical manuals such as RDKit, scikit-learn, and Daylight. The references also cover data resources and benchmarks used for modeling, standardization, and evaluation.
The later chapter references shift toward more specialized and modern topics, including malaria drug discovery, RNA-targeting chemistry, protein kinases, structure prediction, molecular generation, docking, active learning, graph neural networks, and protein language models. Overall, the bibliography highlights how the book integrates chemistry, biology, machine learning, and computational tools to support end-to-end drug discovery pipelines, while grounding those methods in both classic literature and recent advances.
F.12 Chapter 12 References
FAQ
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What is Appendix F in the book about?Appendix F collects the reference lists for chapters 1 through 13 of Build AI Drug Discovery Pipelines. The book uses bracketed in-text citations like [1] that map to the numbered references listed in each chapter section.How are in-text citations connected to the references in Appendix F?In-text citations throughout the book use bracketed numbers, such as [1], [2], and so on. These numbers correspond to the numbered entries listed under the appropriate chapter’s reference section in Appendix F.Which chapters have their references included in Appendix F?Appendix F includes chapter reference lists for chapters 1 through 13. Each chapter has its own numbered references organized within the appendix.Are all appendixes’ references collected in Appendix F?No. The appendix text states that Appendixes C, D, and I have their own reference lists at the end of those appendixes. Appendix F specifically covers chapters 1 through 13.Why are the references organized by chapter in Appendix F?The references are organized by chapter so readers can quickly match a citation in a chapter to the correct source list. This makes it easier to trace where a cited method, dataset, or concept came from.What kind of sources appear in the chapter references?The chapter references include journal articles, conference papers, technical manuals, blog posts, software documentation, datasets, and web resources. They cover topics such as drug discovery, cheminformatics, machine learning, molecular design, and protein modeling.Does Appendix F include references for cheminformatics tools like RDKit?Yes. Several chapter reference lists include RDKit documentation and related resources, reflecting the book’s use of cheminformatics tooling for tasks like descriptor calculation, fingerprints, conformer generation, and molecular analysis.Are datasets and benchmarks cited in Appendix F?Yes. The references include datasets and benchmark resources such as Therapeutics Data Commons, AqSolDB, MOSES, ZINC20, and other domain-specific collections used for model training, evaluation, or screening.Do the references in Appendix F cover AI and machine learning methods?Yes. Many citations point to machine learning methods and papers such as linear models, support vector regression, deep learning, graph neural networks, variational autoencoders, reinforcement learning, and active learning.How should a reader use Appendix F while studying the book?A reader can use Appendix F to look up the full source behind a bracketed citation, verify a method or dataset mentioned in a chapter, and explore related papers and tools for deeper study.
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