LIGHTHOUSE illuminates therapeutics for a variety of diseases including COVID-19

One of the bottlenecks in the application of basic research findings to patients is the enormous cost, time, and effort required for high-throughput screening of potential drugs for given therapeutic targets. Here we have developed LIGHTHOUSE, a graph-based deep learning approach for discovery of the hidden principles underlying the association of small-molecule compounds with target proteins. Without any 3D structural information for proteins or chemicals, LIGHTHOUSE estimates protein-compound scores that incorporate known evolutionary relations and available experimental data. It identified therapeutics for cancer, lifestyle related disease, and bacterial infection. Moreover, LIGHTHOUSE predicted ethoxzolamide as a therapeutic for coronavirus disease 2019 (COVID-19), and this agent was indeed effective against alpha, beta, gamma, and delta variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that are rampant worldwide. We envision that LIGHTHOUSE will help accelerate drug discovery and fill the gap between bench side and bedside.

Automated summary

In this study, a graph-based deep learning approach called LIGHTHOUSE was developed to discover the hidden principles underlying the association of small-molecule compounds with target proteins. LIGHTHOUSE estimated protein-compound scores without relying on 3D structural information, incorporating known evolutionary relations and available experimental data. It successfully identified therapeutics for various diseases and predicted ethoxzolamide as a therapeutic for COVID-19, which was proven effective against different variants of SARS-CoV-2.