BOOK

Essential ORGANIC CHEMISTRY

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Chemical synthesis is foundational across fields like pharmaceuticals, materials, and energy, yet current workflows often rely on repetitive trial-and-error, making them time-consuming and resource-intensive. To overcome these limitations, modern large language models (LLMs), exemplified by GPT‑4, have been fine‑tuned on millions of domain-specific Q&A pairs, creating a specialized assistant—Chemma. Chemma outperforms previous methods in key tasks such as single‑step retrosynthesis planning and yield prediction, demonstrating superior accuracy and efficiency. When integrated with active learning and Bayesian optimizations, it autonomously navigated reaction spaces, notably achieving a 67% isolated yield in an unreported Suzuki–Miyaura cross‑coupling within just 15 iterations—without recourse to quantum‑chemical computation
arxiv.org
. These findings underscore the transformative potential of AI‑driven tools in streamlining organic synthesis, enhancing experimental design, and fostering human–machine collaboration for rapid scientific discovery.

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Detail Information

Series Title

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Call Number

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Publisher

New York : PEARSON EDUCATO., 2006

Collation

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Language

Arabic

ISBN/ISSN

0-13-198483-7

Classification

NONE

Content Type

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Media Type

unmediated

Carrier Type

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Edition

1st edition

Subject(s)

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Specific Detail Info

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Statement of Responsibility

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