Developing novel ligands with enhanced binding affinity for the
  sphingosine 1-phosphate receptor 1 using machine learning
Colin Zhang Yang Ha
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.