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NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

Published on: June 4, 2021

Machine Learning-driven Fragment-based Discovery of CIB1-directed Anti-Tumor Agents by FRASE-bot.

Yi An¹, Marta Glavatskikh¹, Jiwoong Lim¹

¹Center for Integrative Chemical Biology and Drug Discovery, UNC Eshelman School of Pharmacy, University of North Carolina, Chapel Hill, NC 27513.

Research Square

|August 30, 2023

Summary

This summary is machine-generated.

FRASE-bot uses machine learning to find drug fragments for hard-to-drug proteins like CIB1. This computational approach identified the first small-molecule ligand for CIB1, showing cancer cell-killing activity.

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Last Updated: Jul 17, 2025

NMR-Based Fragment Screening in a Minimum Sample but Maximum Automation Mode

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Workflow and Tools for Crystallographic Fragment Screening at the Helmholtz-Zentrum Berlin

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Area of Science:

Computational chemistry
Drug discovery
Molecular biology

Background:

Identifying chemical probes for novel therapeutic targets, especially hard-to-drug proteins, is challenging.
Calcium and Integrin Binding protein 1 (CIB1) is a promising drug target for triple-negative breast cancer but lacks known ligands.
CIB1's function relies on protein-protein interactions and it lacks a natural ligand-binding pocket.

Conclusions:

FRASE-bot is an effective computational strategy for drug discovery against ligand-orphan targets.
The identified CIB1 ligand represents a potential therapeutic lead for triple-negative breast cancer.
Targeting CIB1 with small molecules offers a novel therapeutic approach for specific cancer types.