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Related Experiment Video

Updated: Sep 19, 2025

A Pipeline to Investigate the Structures and Signaling Pathways of Sphingosine 1-Phosphate Receptors

A Pipeline to Investigate the Structures and Signaling Pathways of Sphingosine 1-Phosphate Receptors

Published on: June 8, 2022

Machine learning framework coupled with CADD for predicting sphingosine kinase 1 inhibitors.

Mushtaq Ahmad Wani¹, Pooja Kumari¹, Amit Nargotra¹

¹Discovery Informatics Group, NPMC Division, CSIR-Indian Institute of Integrative Medicine, Jammu, 180001, India.

Computers in Biology and Medicine

|June 5, 2025

Summary

Machine learning models identified potential inhibitors for sphingosine kinase 1 (SphK1), a key target in cancer and cardiovascular diseases. Computational simulations confirmed stable binding and drug-like properties, accelerating therapeutic discovery.

Keywords:

Classification models Machine learning Molecular dynamics simulations Molecular features SphK1

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Published on: October 10, 2018

Area of Science:

Computational chemistry and cheminformatics
Drug discovery and medicinal chemistry

Background:

Sphingosine kinase 1 (SphK1) is a critical target for treating cancer, cardiovascular diseases, and inflammation due to its role in disease progression and resistance.
Developing novel SphK1 inhibitors is essential for therapeutic innovation in these areas.

Purpose of the Study:

To develop and validate machine learning models for predicting potential SphK1 inhibitors.
To identify and characterize novel SphK1 inhibitors using computational methods, including molecular dynamics and binding energy calculations.
To assess the drug-like properties and therapeutic potential of identified inhibitors.

Main Methods:

Machine learning models (Random Forest, AdaBoost, etc.) were trained and validated to predict SphK1 inhibitors.
Molecular dynamics simulations (100 ns) were performed to assess the stability of compounds within the SphK1 binding pocket.
Molecular mechanics with generalized Born surface area (MM/GBSA) calculations were used to predict binding energies.
In silico drug-likeness assessments were conducted for top-ranked compounds.

Main Results:

Random Forest model achieved 89.81% accuracy, identifying 187 potential SphK1 inhibitors from an in-house library.
Molecular dynamics simulations showed stable binding for compounds IS01027, IS01265, and IS00998.
MM/GBSA calculations indicated strong binding affinities for PF-543, IS01265, IS01027, and IS00998.
Top compounds exhibited favorable drug-like characteristics, including good absorption and solubility, with IS00998 showing excellent solubility.

Conclusions:

The integrated computational approach effectively accelerates the discovery of SphK1 inhibitors.
Identified compounds demonstrate significant potential as therapeutic agents for SphK1-related diseases.
This study provides valuable insights for future drug development targeting SphK1.