{"id":4832,"date":"2026-03-11T14:46:15","date_gmt":"2026-03-11T12:46:15","guid":{"rendered":"https:\/\/blog.molport.com\/?p=4832"},"modified":"2026-03-31T17:26:30","modified_gmt":"2026-03-31T15:26:30","slug":"first-qsar-equation-molport-chronicles-from-alchemy-to-pharma-no-11","status":"publish","type":"post","link":"https:\/\/blog.molport.com\/news\/first-qsar-equation-molport-chronicles-from-alchemy-to-pharma-no-11\/","title":{"rendered":"Molport Chronicles- from Alchemy to Pharma No.11"},"content":{"rendered":"

First QSAR equation<\/h2>\n

This time: a historical MedChem milestone – the birth of QSAR in 1961, long before AI, a breakthrough that laid the foundation for today\u2019s MedChem, CADD, and AI revolution<\/p>\n

There are moments in medicinal chemistry where the field doesn\u2019t just progress, it changes its language. In July 1961, Corwin Hansch, the father of the computer-assisted design,\u00a0(with Toshio Fujita) formulated what is widely considered the first QSAR equation, a quantitative way to connect molecular properties to biological activity.<\/p>\n

It emerged from more than a decade of frustration trying to rationalize structure-activity relationships (SAR) for plant growth regulators using classical physical-organic tools alone. Soon after 1961 the idea accelerated: activity could be modeled, compared, optimized, not only \u201cexplained after the fact\u201d. Before QSAR, MedChem was already brilliant but often qualitative:
\n\u2022\u00a0\u201cThis substituent seems better\u201d
\n\u2022\u00a0\u201cAdding lipophilicity helps (sometimes)\u201d
\n\u2022 \u201cElectronics might matter\u201d<\/p>\n

\"\"
\nHansch\u2019s shift was bold (at that time, no AI tools at all):\u00a0Treat biological activity like a property you can regress against physicochemical descriptors.
\nHydrophobicity, electronics, and steric – as measurable variables, not just intuition. This is the conceptual ancestor of Hansch analysis, and in a very real sense, a foundational piece of what we now call computer-aided drug design (CADD).
\nWho was Corwin Hansch? He was trained in organic chemistry, worked during WWII, and later spent his career at Pomona College, where he continued to push quantitative thinking into chemical biology. Before becoming the \u201cQSAR guy,\u201d he worked as a group leader on the Manhattan Project in WWII-era research. Hansch is one of those rare people whose name became a verb in the community: \u201cDo a Hansch analysis\u201d = classic QSAR regression thinking. That\u2019s peak scientific immortality.\u00a0The impact we\u2019re still living with (and how it connects to AI drug discovery).<\/p>\n

QSAR didn\u2019t replace medicinal chemistry intuition; it made it measurable, testable, and improvable.\u00a0It shaped the way we still work today:
\n\u2022\u00a0rational lead optimization
\n\u2022\u00a0property-driven design
\n\u2022\u00a0toxicity prediction and risk modeling
\n\u2022\u00a0chemoinformatics and descriptor-based thinking<\/p>\n

Here\u2019s the real bridge to modern AI-driven drug discovery:\u00a0AI models didn\u2019t appear out of thin air – they inherited QSAR\u2019s core idea:\u00a0biological activity can be learned from molecular features. We\u2019ve moved from linear regressions and handcrafted descriptors to deep learning and foundation models but the mindset is the same:\u00a0turn chemistry into signals, and signals into predictions.<\/p>\n

Even classics like the hydrophobic substituent constant (\u03c0) helped cement the idea that properties drive potency – a principle still embedded in today\u2019s predictive pipelines.
\nFrom \u201cmake-and-test\u201d \u2192 \u201cdesign-and-test\u201d \u2192 \u201cpredict-and-design\u201d
\nThat\u2019s the legacy.<\/p>\n

Other Molport Chronicles posts- from Alchemy to Pharma:<\/strong><\/p>\n