Fall 2026

Natural Product Synthesis, Biosynthesis and Drug Discovery

Covers synthesis and biosynthesis of natural products for drug discovery, including biosynthetic pathways and bioorganic chemistry.

no offering record for this termCredits unavailable
Recorded instructors · Fall 2026 No instructors listed

Summary

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Recent recorded grades — Spring 2017: 3.50 GPA, 83.3% A/AB (n=12 letter grades); Spring 2019: 3.77 GPA, 80.0% A/AB (n=15 letter grades); Spring 2025: 3.54 GPA, 76.9% A/AB (n=13 letter grades).

Grade history

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average GPA
letter grades
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AB
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Prerequisites

Course map

CHEM 345 and BIOCHEM 508

PHMSCI 786

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Professors

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

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Calendar & sections

Fall 2026

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Meeting source records

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Student experience

the class

Recent recorded grades — Spring 2017: 3.50 GPA, 83.3% A/AB (n=12 letter grades); Spring 2019: 3.77 GPA, 80.0% A/AB (n=15 letter grades); Spring 2025: 3.54 GPA, 76.9% A/AB (n=13 letter grades).

difficulty & workload

No workload feedback recorded.

Topics

  • Natural products in drug discovery
  • Biosynthetic pathways and synthetic strategies
  • Bioorganic chemistry and enzyme mechanisms

Skills

  • Synthesis and biosynthesis of natural products
  • Developing synthetic strategies for natural products
  • Understanding bioorganic chemistry and enzyme mechanisms

Grades

Latest available · Spring 2025— not enough history to project Fall 2026.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2025

More grade details Grade mix, volume & source data

Not enough comparable courses for Fall 2026 in UW–Madison.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

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Grade history

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Dataset & model

Recorded 2026-09-07

View the dataset on Hugging Face
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
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Full model traces

Recorded model configuration, reasoning, and tool conversations.

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Model & dataset provenance 
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "37062cb14a5ce2f8999077dcf235ac78116044f1ba0a7e618dc2ee3d3d47f012",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}