Developing novel ligands with enhanced binding affinity for the sphingosine 1-phosphate receptor 1 using machine learning

TL;DR

This study uses machine learning to rapidly generate and identify new ligands with higher predicted binding affinity for S1PR1, a target for multiple sclerosis treatment, leading to promising drug candidates.

Contribution

The paper introduces a finetuned autoencoder model that efficiently generates novel ligands with improved binding affinity for S1PR1, advancing drug discovery methods.

Findings

Generated over 500 molecular variants in under an hour

Identified 25 compounds with higher predicted affinity

Discovered 6 promising drug-like candidates

Abstract

Multiple sclerosis (MS) is a debilitating neurological disease affecting nearly one million people in the United States. Sphingosine-1-phosphate receptor 1, or S1PR1, is a protein target for MS. Siponimod, a ligand of S1PR1, was approved by the FDA in 2019 for MS treatment, but there is a demonstrated need for better therapies. To this end, we finetuned an autoencoder machine learning model that converts chemical formulas into mathematical vectors and generated over 500 molecular variants based on siponimod, out of which 25 compounds had higher predicted binding affinity to S1PR1. The model was able to generate these ligands in just under one hour. Filtering these compounds led to the discovery of six promising candidates with good drug-like properties and ease of synthesis. Furthermore, by analyzing the binding interactions for these ligands, we uncovered several chemical properties that contribute to high binding affinity to S1PR1. This study demonstrates that machine learning can accelerate the drug discovery process and reveal new insights into protein-drug interactions.