Machine Learning Models Identify Inhibitors of SARS-CoV-2.

With the rapidly evolving SARS-CoV-2 variants of concern, there is an urgent need for the discovery of further treatments for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need, and numerous compounds have already been selected for in vitro testing by several groups. These have led to a growing database of molecules with in vitro activity against the virus. Machine learning models can assist drug discovery through prediction of the best compounds based on previously published data. Herein, we have implemented several machine learning methods to develop predictive models from recent SARS-CoV-2 in vitro inhibition data and used them to prioritize additional FDA-approved compounds for in vitro testing selected from our in-house compound library. From the compounds predicted with a Bayesian machine learning model, lumefantrine, an antimalarial was selected for testing and showed limited antiviral activity in cell-based assays while demonstrating binding (Kd 259 nM) to the spike protein using microscale thermophoresis. Several other compounds which we prioritized have since been tested by others and were also found to be active in vitro. This combined machine learning and in vitro testing approach can be expanded to virtually screen available molecules with predicted activity against SARS-CoV-2 reference WIV04 strain and circulating variants of concern. In the process of this work, we have created multiple iterations of machine learning models that can be used as a prioritization tool for SARS-CoV-2 antiviral drug discovery programs. The very latest model for SARS-CoV-2 with over 500 compounds is now freely available at www.assaycentral.org.

Self-Masked Aldehyde Inhibitors: A Novel Strategy for Inhibiting Cysteine Proteases.

Cysteine proteases comprise an important class of drug targets, especially for infectious diseases such as Chagas disease (cruzain) and COVID-19 (3CL protease, cathepsin L). Peptide aldehydes have proven to be potent inhibitors for all of these proteases. However, the intrinsic, high electrophilicity of the aldehyde group is associated with safety concerns and metabolic instability, limiting the use of aldehyde inhibitors as drugs. We have developed a novel class of self-masked aldehyde inhibitors (SMAIs) for cruzain, the major cysteine protease of the causative agent of Chagas disease-Trypanosoma cruzi. These SMAIs exerted potent, reversible inhibition of cruzain (Ki* = 18-350 nM) while apparently protecting the free aldehyde in cell-based assays. We synthesized prodrugs of the SMAIs that could potentially improve their pharmacokinetic properties. We also elucidated the kinetic and chemical mechanism of SMAIs and applied this strategy to the design of anti-SARS-CoV-2 inhibitors.