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Related Experiment Video

Updated: Jul 11, 2026

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Nano-Differential Scanning Fluorimetry for Screening in Fragment-based Lead Discovery

Published on: May 16, 2021

Tagged fragment method for evolutionary structure-based de novo lead generation and optimization.

Qian Liu¹, Brian Masek, Karl Smith

¹Tripos, Inc., 1699 South Hanley Road, St. Louis, Missouri 63144, USA. qliu@tripos.com

Journal of Medicinal Chemistry

|October 9, 2007

Summary

A new computer-assisted drug design method, EAISFD, integrates EA-Inventor and Surflex-Dock for novel ligand discovery. This approach effectively generates diverse, targeted molecular structures for lead generation and optimization, as demonstrated with p38 MAP kinase.

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Area of Science:

Computational chemistry
Medicinal chemistry
Drug discovery

Background:

De novo drug design is crucial for identifying novel therapeutic agents.
Existing methods may lack efficiency in generating diverse and targeted molecular scaffolds.
Accurate scoring functions are essential for predicting ligand-target interactions.

Purpose of the Study:

To introduce EAISFD, a novel computer-assisted de novo drug design method.
To integrate a de novo design engine (EA-Inventor) with a molecular docking scoring function (Surflex-Dock).
To enable the generation of diverse and targeted ligand structures for drug discovery.

Main Methods:

Utilized tagged fragments within EA-Inventor for substructure matching in Surflex-Dock.
Employed a target score mechanism to guide the generation of diverse structures.
Applied the EAISFD method to design ligands for p38 MAP kinase.

Main Results:

EAISFD successfully identified known inhibitor scaffolds for p38 MAP kinase.
The method generated novel scaffold types beyond known inhibitors.
Demonstrated the capability to produce a diverse set of desired molecular structures.

Conclusions:

EAISFD is an effective computer-assisted de novo drug design strategy.
The method facilitates both lead generation and lead optimization.
EAISFD shows promise for discovering novel drug candidates, including for targets like p38 MAP kinase.