Fundamentals of Organic Chemistry

By J. Mcmurry. 2019

128 citations recorded by the source corpus.

Written for the short course-where content must be thorough, but to-the-point, FUNDAMENTALS OF ORGANIC CHEMISTRY, Fifth Edition provides an effective, clear, and readable introduction to the beauty and logic of organic chemistry. McMurry presents only those subjects needed for a brief course while maintaining the important pedagogical tools commonly found in larger books. With clear explanations, thought-provoking examples, and an innovative vertical format for explaining reaction mechanisms, FUNDAMENTALS takes a modern approach: primary organization is by functional group, beginning with the simple (alkanes) and progressing to the more complex. Within the primary organization, there is also an emphasis on explaining the fundamental mechanistic similarities of reactions. Through this approach, memorization is minimized and understanding is maximized. This new edition represents a major revision. The text has been revised at the sentence level to further improve clarity and readability; many new examples and topics of biological relevance have been added; and many new features have been introduced.

Subfields: Physical and Theoretical Chemistry, Computational Chemistry & Drug Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Physical and Theoretical Chemistry

1. Percy Liang
2. Joseph Gomes
3. Jure Leskovec
4. Vijay S. Pande
5. Marinka Zitnik
6. Bowen Liu
7. Weihua Hu

In Computational Chemistry & Drug Discovery

1. Jure Leskovec
2. Bowen Liu
3. Joseph Gomes
4. Marinka Zitnik
5. Weihua Hu
6. Percy Liang
7. Vijay S. Pande
8. Chaopeng Shen

Citations in the corpus

• Strategies for Pre-training Graph Neural Networks (2019, methodology): “t atom types of masked atoms, as illustrated in Figure 2 (b). This forces the model to capture chemistry domain knowledge, such as valency and the electronic or steric properties of functional groups [30]. Graph-level Prediction. To learn composable node embeddings that are useful for downstream tas”
• Pre-training Graph Neural Networks (2019, methodology): “This forces the model to capture chemistry domain knowledge, such as valency and the electronic or steric properties of functional groups [30].”