In Vitro Characterization and Rescue of VX Metabolism in Human Liver Microsomes.

Venomous agent X (VX) is an organophosphate acetylcholinesterase (AChE) inhibitor, and although it is one of the most toxic AChE inhibitors known, the extent of metabolism in humans is not currently well understood. The known metabolism in humans is limited to the metabolite identification from a single victim of the Osaka poisoning in 1994, which allowed for the identification of several metabolic products. VX has been reported to be metabolized in vitro by paraoxonase-1 and phosphotriesterase, although their binding constants are many orders of magnitude above the LD50, suggesting limited physiologic relevance. Using incubation with human liver microsomes (HLMs), we have now characterized the metabolism of VX and the formation of multiple metabolites as well as identified a Food and Drug Administration-approved drug [ethylenediaminetetraacetic acid (EDTA)] that enhances the metabolic rate. HLM incubation alone shows a pronounced increase in the metabolism of VX compared with buffer, suggesting that cytochrome P450-mediated metabolism of VX is occurring. We identified a biphasic decay with two distinct rates of metabolism. The enhancement of VX metabolism in multiple buffers was assessed to attempt to mitigate the effect of hydrolysis rates. The formation of VX metabolites was shown to be shifted with HLMs, suggesting a pathway enhancement over simple hydrolysis. Additionally, our investigation of hydrolysis rates in various common buffers used in biologic assays discovered dramatic differences in VX stability. The new human in vitro VX metabolic data reported points to a potential in vivo treatment strategy (EDTA) for rescue in individuals that are poisoned though enhancement of metabolism alongside existing treatments. SIGNIFICANCE STATEMENT: Venomous agent X (VX) is a potent acetylcholinesterase inhibitor and chemical weapon. To date, we do not possess a clear understanding of its metabolism in humans that would assist us in treating those exposed to it. This study now describes the human liver microsomal metabolism of VX and identifies ethylenediaminetetraacetic acid, which appears to enhance the rate of metabolism. This may provide a potential treatment option for human VX poisoning.

Sequential Contrastive and Deep Learning Models to Identify Selective Butyrylcholinesterase Inhibitors.

Butyrylcholinesterase (BChE) is a target of interest in late-stage Alzheimer's Disease (AD) where selective BChE inhibitors (BIs) may offer symptomatic treatment without the harsh side effects of acetylcholinesterase (AChE) inhibitors. In this study, we explore multiple machine learning strategies to identify BIs in silico, optimizing for precision over all other metrics. We compare state-of-the-art supervised contrastive learning (CL) with deep learning (DL) and Random Forest (RF) machine learning, across single and sequential modeling configurations, to identify the best models for BChE selectivity. We used these models to virtually screen a vendor library of 5 million compounds for BIs and tested 20 of these compounds in vitro. Seven of the 20 compounds displayed selectivity for BChE over AChE, reflecting a hit rate of 35% for our model predictions, suggesting a highly efficient strategy for modeling selective inhibition.

Computational drug repositioning identifies niclosamide and tribromsalan as inhibitors of Mycobacterium tuberculosis and Mycobacterium abscessus.

Tuberculosis (TB) is still a major global health challenge, killing over 1.5 million people each year, and hence, there is a need to identify and develop novel treatments for Mycobacterium tuberculosis (M. tuberculosis). The prevalence of infections caused by nontuberculous mycobacteria (NTM) is also increasing and has overtaken TB cases in the United States and much of the developed world. Mycobacterium abscessus (M. abscessus) is one of the most frequently encountered NTM and is difficult to treat. We describe the use of drug-disease association using a semantic knowledge graph approach combined with machine learning models that has enabled the identification of several molecules for testing anti-mycobacterial activity. We established that niclosamide (M. tuberculosis IC90 2.95 µM; M. abscessus IC90 59.1 µM) and tribromsalan (M. tuberculosis IC90 76.92 µM; M. abscessus IC90 147.4 µM) inhibit M. tuberculosis and M. abscessus in vitro. To investigate the mode of action, we determined the transcriptional response of M. tuberculosis and M. abscessus to both compounds in axenic log phase, demonstrating a broad effect on gene expression that differed from known M. tuberculosis inhibitors. Both compounds elicited transcriptional responses indicative of respiratory pathway stress and the dysregulation of fatty acid metabolism.

1-Sulfonyl-3-amino-1H-1,2,4-triazoles as Yellow Fever Virus Inhibitors: Synthesis and Structure-Activity Relationship.

Yellow fever virus (YFV) transmitted by infected mosquitoes causes an acute viral disease for which there are no approved small-molecule therapeutics. Our recently developed machine learning models for YFV inhibitors led to the selection of a new pyrazolesulfonamide derivative RCB16003 with acceptable in vitro activity. We report that the N-phenyl-1-(phenylsulfonyl)-1H-1,2,4-triazol-3-amine class, which was recently identified as active non-nucleoside reverse transcriptase inhibitors against HIV-1, can also be repositioned as inhibitors of yellow fever virus replication. As compared to other Flaviviridae or Togaviridae family viruses tested, both compounds RCB16003 and RCB16007 demonstrate selectivity for YFV over related viruses, with only RCB16007 showing some inhibition of the West Nile virus (EC50 7.9 µM, CC50 17 µM, SI 2.2). We also describe the absorption, distribution, metabolism, and excretion (ADME) in vitro and pharmacokinetics (PK) for RCB16007 in mice. This compound had previously been shown to not inhibit hERG, and we now describe that it has good metabolic stability in mouse and human liver microsomes, low levels of CYP inhibition, high protein binding, and no indication of efflux in Caco-2 cells. A single-dose oral PK study in mice has a T1/2 of 3.4 h and Cmax of 1190 ng/mL, suggesting good availability and stability. We now propose that the N-phenyl-1-(phenylsulfonyl)-1H-1,2,4-triazol-3-amine class may be prioritized for in vivo efficacy testing against YFV.

High-Throughput Phenotypic Screening and Machine Learning Methods Enabled the Selection of Broad-Spectrum Low-Toxicity Antitrypanosomatidic Agents.

Broad-spectrum anti-infective chemotherapy agents with activity against Trypanosomes, Leishmania, and Mycobacterium tuberculosis species were identified from a high-throughput phenotypic screening program of the 456 compounds belonging to the Ty-Box, an in-house industry database. Compound characterization using machine learning approaches enabled the identification and synthesis of 44 compounds with broad-spectrum antiparasitic activity and minimal toxicity against Trypanosoma brucei, Leishmania Infantum, and Trypanosoma cruzi. In vitro studies confirmed the predictive models identified in compound 40 which emerged as a new lead, featured by an innovative N-(5-pyrimidinyl)benzenesulfonamide scaffold and promising low micromolar activity against two parasites and low toxicity. Given the volume and complexity of data generated by the diverse high-throughput screening assays performed on the compounds of the Ty-Box library, the chemoinformatic and machine learning tools enabled the selection of compounds eligible for further evaluation of their biological and toxicological activities and aided in the decision-making process toward the design and optimization of the identified lead.

Synthesis and Evaluation of 9-Aminoacridines with SARS-CoV-2 Antiviral Activity.

There have been relatively few small molecules developed with direct activity against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Two existing antimalarial drugs, pyronaridine and quinacrine, display whole cell activity against SARS-CoV-2 in A549 + ACE2 cells (pretreatment, IC50 = 0.23 and 0.19 µM, respectively) with moderate cytotoxicity (CC50 = 11.53 and 9.24 µM, respectively). Moreover, pyronaridine displays in vitro activity against SARS-CoV-2 PLpro (IC50 = 1.8 µM). Given their existing antiviral activity, these compounds are strong candidates for repurposing against COVID-19 and prompt us to study the structure-activity relationship of the 9-aminoacridine scaffold against SARS-CoV-2 using traditional medicinal chemistry to identify promising new analogs. Our studies identified several novel analogs possessing potent in vitro activity in U2-OS ACE2 GFP 1-10 and 1-11 (IC50 < 1.0 µM) as well as moderate cytotoxicity (CC50 > 4.0 µM). Compounds such as 7g, 9c, and 7e were more active, demonstrating selectivity indices SI > 10, and 9c displayed the strongest activity (IC50 ≤ 0.42 µM, CC50 ≥ 4.41 µM, SI > 10) among them, indicating that it has potential as a new lead molecule in this series against COVID-19.

Antiviral Evaluation of Dispirotripiperazines against Hepatitis B Virus.

Hepatitis B virus (HBV) is a hepatotropic DNA virus that replicates by reverse transcription. It chronically infects >296 million people worldwide, including ∼850,000 in the USA, and kills 820,000 annually worldwide. Current nucleos(t)ide analogue (NA) or pegylated interferon α therapies do not eradicate the virus and would benefit from a complementary antiviral drug. We performed a preliminary screen of 28 dispirotripiperazines against HBV, identifying 9 hits with EC50 of 0.7-25 µM. Compound 11826096 displays the most potent activity and represents a promising lead for future optimization. While the mechanism of action is unknown, preliminary assays limit possible targets to activities involved in RNA accumulation, translation, capsid assembly, and/or capsid stability. In addition, we built machine learning models to determine if they were able to predict the activity of this series of compounds. The novelty of these molecules indicated they were outside of the applicability domain of these models.

In silico ADME/tox comes of age: twenty years later.

In the early 2000s pharmaceutical drug discovery was beginning to use computational approaches for absorption, distribution, metabolism, excretion and toxicity (ADME/Tox, also known as ADMET) prediction. This emphasis on prediction was an effort to reduce the risk of later stage failures from ADME/Tox.Much has been written in the intervening twenty plus years and significant expenditure has occurred in companies developing these in silico capabilities which can be gleaned from publications. It is therefore an appropriate time to briefly reflect on what was proposed then and what the reality is today.20 years ago, we tended to optimise bioactivity and perhaps one ADME/Tox property at a time. Previously pharmaceutical companies needed a whole infrastructure for models - in silico and in vitro experts, IT, champions on a project team, educators and management support. Now we are in the age of generative de novo design where bioactivity and many ADME/Tox properties can be optimised and large language model technologies are available.There are also some challenges such as the focus on very large molecules which may be outside of current ADME/Tox models.We provide an opportunity to look forward with the increasing public data for ADME/Tox as well as expanded types of algorithms available.

Human CYP2C19 Substrate and Inhibitor Characterization of Organophosphate Pesticides.

CYP2C19 is an important enzyme for organophosphate pesticide (OPP) metabolism. Because the OPPs can be both substrates and inhibitors of CYP2C19, we screened 45 OPPs for their ability to inhibit the activity of this enzyme and investigated the role of CYP2C19 in the metabolism of 22 of these molecules. We identified several nanomolar inhibitors of CYP2C19 as well as determined that thions, in general, are more potent inhibitors than oxons. We also determined that thions are readily metabolized by CYP2C19, although we saw no relationship between IC50 values and intrinsic clearance rates. This study may have implications for mitigating the risk of OPP poisoning.

Learning from COVID-19: How drug hunters can prepare for the next pandemic.

Over 3 years, the SARS-CoV-2 pandemic killed nearly 7 million people and infected more than 767 million globally. During this time, our very small company was able to contribute to antiviral drug discovery efforts through global collaborations with other researchers, which enabled the identification and repurposing of multiple molecules with activity against SARS-CoV-2 including pyronaridine tetraphosphate, tilorone, quinacrine, vandetanib, lumefantrine, cetylpyridinium chloride, raloxifene, carvedilol, olmutinib, dacomitinib, crizotinib, and bosutinib. We highlight some of the key findings from this experience of using different computational and experimental strategies, and detail some of the challenges and strategies for how we might better prepare for the next pandemic so that potential antiviral treatments are available for future outbreaks.

Comparing LD50/LC50 Machine Learning Models for Multiple Species.

The lethal dose or concentration which kills 50% of the animals (LD50 or LC50) is an important parameter for scientists to understand the toxicity of chemicals in different scenarios that can be used to make go-no-go decisions, and ultimately assist in the choice of the right personal protective equipment needed for containment. The LD50 assessment process has also required the use of many animals although modern methods have reduced the number of rats needed. Since a compound is usually considered highly toxic when the LD50 is lower than 25 mg/kg, such a classification provides potentially valuable safety information to synthetic chemists and other safety assessment scientists. The need for finding alternative approaches such as computational methods is important to ultimately reduce animal use for this testing further still. We now summarize our efforts to use public data for building in vivo LD50 or LC50 classification and regression machine learning models for various species (rat, mouse, fish and daphnia) and their 5-fold cross validation statistics with different machine learning algorithms as well as an external curated test set for mouse LD50. These datasets consist of different molecule classes, may cover different activity ranges, and also have a range of dataset sizes. The challenges of using such computational models are that their applicability domain will also need to be understood so that they can be used to make reliable predictions for novel molecules. These machine learning models will also need to be backed up with experimental validation. However, such models could also be used for efforts to bridge gaps in individual toxicity datasets. Making such models available also opens them up to potential misuse or dual use. We will summarize these efforts and propose that they could be used for scoring the millions of commercially available molecules, most of which likely do not have a known LD50 or for that matter any data in vitro or in vivo for toxicity.

Efficacy of an isoxazole-3-carboxamide analog of pleconaril in mouse models of Enterovirus-D68 and Coxsackie B5.

Enteroviruses (EV) cause a number of life-threatening infectious diseases. EV-D68 is known to cause respiratory illness in children that can lead to acute flaccid myelitis. Coxsackievirus B5 (CVB5) is commonly associated with hand-foot-mouth disease. There is no antiviral treatment available for either. We have developed an isoxazole-3-carboxamide analog of pleconaril (11526092) which displayed potent inhibition of EV-D68 (IC50 58 nM) as well as other enteroviruses including the pleconaril-resistant Coxsackievirus B3-Woodruff (IC50 6-20 nM) and CVB5 (EC50 1 nM). Cryo-electron microscopy structures of EV-D68 in complex with 11526092 and pleconaril demonstrate destabilization of the EV-D68 MO strain VP1 loop, and a strain-dependent effect. A mouse respiratory model of EV-D68 infection, showed 3-log decreased viremia, favorable cytokine response, as well as statistically significant 1-log reduction in lung titer reduction at day 5 after treatment with 11526092. An acute flaccid myelitis neurological infection model did not show efficacy. 11526092 was tested in a mouse model of CVB5 infection and showed a 4-log TCID50 reduction in the pancreas. In summary, 11526092 represents a potent in vitro inhibitor of EV with in vivo efficacy in EV-D68 and CVB5 animal models suggesting it is worthy of further evaluation as a potential broad-spectrum antiviral therapeutic against EV.

Transporter Inhibition Profile for the Antivirals Tilorone, Quinacrine and Pyronaridine.

Pyronaridine, tilorone and quinacrine are cationic molecules that have in vitro activity against Ebola, SARS-CoV-2 and other viruses. All three molecules have also demonstrated in vivo activity against Ebola in mice, while pyronaridine showed in vivo efficacy against SARS-CoV-2 in mice. We have recently tested these molecules and other antivirals against human organic cation transporters (OCTs) and apical multidrug and toxin extruders (MATEs). Quinacrine was found to be an inhibitor of OCT2, while tilorone and pyronaridine were less potent, and these displayed variability depending on the substrate used. To assess whether any of these three molecules have other potential interactions with additional transporters, we have now screened them at 10 µM against various human efflux and uptake transporters including P-gp, OATP1B3, OAT1, OAT3, MRP1, MRP2, MRP3, BCRP, as well as confirmational testing against OCT1, OCT2, MATE1 and MATE2K. Interestingly, in this study tilorone appears to be a more potent inhibitor of OCT1 and OCT2 than pyronaridine or quinacrine. However, both pyronaridine and quinacrine appear to be more potent inhibitors of MATE1 and MATE2K. None of the three compounds inhibited MRP1, MRP2, MRP3, OAT1, OAT3, P-gp or OATP1B3. Similarly, we previously showed that tilorone and pyronaridine do not inhibit OATP1B1 and have confirmed that quinacrine behaves similarly. In total, these observations suggest that the three compounds only appear to interact with OCTs and MATEs to differing extents, suggesting they may be involved in fewer clinically relevant drug-transporter interactions involving pharmaceutical substrates of the other major transporters tested.

Targeted Metabolomics of Organophosphate Pesticides and Chemical Warfare Nerve Agent Simulants Using High- and Low-Dose Exposure in Human Liver Microsomes.

Our current understanding of organophosphorus agent (pesticides and chemical warfare nerve agents) metabolism in humans is limited to the general transformation by cytochrome P450 enzymes and, to some extent, by esterases and paraoxonases. The role of compound concentrations on the rate of clearance is not well established and is further explored in the current study. We discuss the metabolism of 56 diverse organophosphorus compounds (both pesticides and chemical warfare nerve agent simulants), many of which were explored at two variable dose regimens (high and low), determining their clearance rates (Clint) in human liver microsomes. For compounds that were soluble at high concentrations, 1D-NMR, 31P, and MRM LC-MS/MS were used to calculate the Clint and the identity of certain metabolites. The determined Clint rates ranged from 0.001 to 2245.52 µL/min/mg of protein in the lower dose regimen and from 0.002 to 98.57 µL/min/mg of protein in the high dose regimen. Though direct equivalency between the two regimens was absent, we observed (1) both mono- and bi-phasic metabolism of the OPs and simulants in the microsomes. Compounds such as aspon and formothion exhibited biphasic decay at both high and low doses, suggesting either the involvement of multiple enzymes with different KM or substrate/metabolite effects on the metabolism. (2) A second observation was that while some compounds, such as dibrom and merphos, demonstrated a biphasic decay curve at the lower concentrations, they exhibited only monophasic metabolism at the higher concentration, likely indicative of saturation of some metabolic enzymes. (3) Isomeric differences in metabolism (between Z- and E- isomers) were also observed. (4) Lastly, structural comparisons using examples of the oxon group over the original phosphorothioate OP are also discussed, along with the identification of some metabolites. This study provides initial data for the development of in silico metabolism models for OPs with broad applications.

Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species.

Acetylcholinesterase (AChE) is an important enzyme and target for human therapeutics, environmental safety, and global food supply. Inhibitors of this enzyme are also used for pest elimination and can be misused for suicide or chemical warfare. Adverse effects of AChE pesticides on nontarget organisms, such as fish, amphibians, and humans, have also occurred as a result of biomagnifications of these toxic compounds. We have exhaustively curated the public data for AChE inhibition data and developed machine learning classification models for seven different species. Each set of models were built using up to nine different algorithms for each species and Morgan fingerprints (ECFP6) with an activity cutoff of 1 µM. The human (4075 compounds) and eel (5459 compounds) consensus models predicted AChE inhibition activity using external test sets from literature data with 81% and 82% accuracy, respectively, while the reciprocal cross (76% and 82% percent accuracy) was not species-specific. In addition, we also created machine learning regression models for human and eel AChE inhibition to return a predicted IC50 value for a queried molecule. We did observe an improved species specificity in the regression models, where a human support vector regression model of human AChE inhibition (3652 compounds) predicted the IC50s of the human test set to a better extent than the eel regression model (4930 compounds) on the same test set, based on mean absolute percentage error (MAPE = 9.73% vs 13.4%). The predictive power of these models certainly benefits from increasing the chemical diversity of the training set, as evidenced by expanding our human classification model by incorporating data from the Tox21 library of compounds. Of the 10 compounds we tested that were predicted active by this expanded model, two showed >80% inhibition at 100 µM. This machine learning approach therefore offers the ability to rapidly score massive libraries of molecules against the models for AChE inhibition that can then be selected for future in vitro testing to identify potential toxins. It also enabled us to create a public website, MegaAChE, for single-molecule predictions of AChE inhibition using these models at megaache.collaborationspharma.com.

Machine Learning Models Identify New Inhibitors for Human OATP1B1.

The uptake transporter OATP1B1 (SLC01B1) is largely localized to the sinusoidal membrane of hepatocytes and is a known victim of unwanted drug-drug interactions. Computational models are useful for identifying potential substrates and/or inhibitors of clinically relevant transporters. Our goal was to generate OATP1B1 in vitro inhibition data for [3H] estrone-3-sulfate (E3S) transport in CHO cells and use it to build machine learning models to facilitate a comparison of seven different classification models (Deep learning, Adaboosted decision trees, Bernoulli naïve bayes, k-nearest neighbors (knn), random forest, support vector classifier (SVC), logistic regression (lreg), and XGBoost (xgb)] using ECFP6 fingerprints to perform 5-fold, nested cross validation. In addition, we compared models using 3D pharmacophores, simple chemical descriptors alone or plus ECFP6, as well as ECFP4 and ECFP8 fingerprints. Several machine learning algorithms (SVC, lreg, xgb, and knn) had excellent nested cross validation statistics, particularly for accuracy, AUC, and specificity. An external test set containing 207 unique compounds not in the training set demonstrated that at every threshold SVC outperformed the other algorithms based on a rank normalized score. A prospective validation test set was chosen using prediction scores from the SVC models with ECFP fingerprints and were tested in vitro with 15 of 19 compounds (84% accuracy) predicted as active (≥20% inhibition) showed inhibition. Of these compounds, six (abamectin, asiaticoside, berbamine, doramectin, mobocertinib, and umbralisib) appear to be novel inhibitors of OATP1B1 not previously reported. These validated machine learning models can now be used to make predictions for drug-drug interactions for human OATP1B1 alongside other machine learning models for important drug transporters in our MegaTrans software.

Pyronaridine Protects against SARS-CoV-2 Infection in Mouse.

There are currently relatively few small-molecule antiviral drugs that are either approved or emergency-approved for use against severe acute respiratory coronavirus 2 (SARS-CoV-2). One of these is remdesivir, which was originally repurposed from its use against Ebola. We evaluated three molecules we had previously identified computationally with antiviral activity against Ebola and Marburg and identified pyronaridine, which inhibited the SARS-CoV-2 replication in A549-ACE2 cells. The in vivo efficacy of pyronaridine has now been assessed in a K18-hACE transgenic mouse model of COVID-19. Pyronaridine treatment demonstrated a statistically significant reduction of viral load in the lungs of SARS-CoV-2-infected mice, reducing lung pathology, which was also associated with significant reduction in the levels of pro-inflammatory cytokines/chemokine and cell infiltration. Pyronaridine inhibited the viral PLpro activity in vitro (IC50 of 1.8 µM) without any effect on Mpro, indicating a possible molecular mechanism involved in its ability to inhibit SARS-CoV-2 replication. We have also generated several pyronaridine analogs to assist in understanding the structure activity relationship for PLpro inhibition. Our results indicate that pyronaridine is a potential therapeutic candidate for COVID-19.

Machine Learning Models for Mycobacterium tuberculosisIn Vitro Activity: Prediction and Target Visualization.

Tuberculosis (TB) is a major global health challenge, with approximately 1.4 million deaths per year. There is still a need to develop novel treatments for patients infected with Mycobacterium tuberculosis (Mtb). There have been many large-scale phenotypic screens that have led to the identification of thousands of new compounds. Yet, there is very limited investment in TB drug discovery which points to the need for new methods to increase the efficiency of drug discovery against Mtb. We have used machine learning approaches to learn from the public Mtb data, resulting in many data sets and models with robust enrichment and hit rates leading to the discovery of new active compounds. Recently, we have curated predominantly small-molecule Mtb data and developed new machine learning classification models with 18 886 molecules at different activity cutoffs. We now describe the further validation of these Bayesian models using a library of over 1000 molecules synthesized as part of EU-funded New Medicines for TB and More Medicines for TB programs. We highlight molecular features which are enriched in these active compounds. In addition, we provide new regression and classification models that can be used for scoring compound libraries or used to design new molecules. We have also visualized these molecules in the context of known molecular targets and identified clusters in chemical property space, which may aid in future target identification efforts. Finally, we are also making these data sets publicly available, representing a significant increase to the available Mtb inhibition data in the public domain.

Defending Antiviral Cationic Amphiphilic Drugs That May Cause Drug-Induced Phospholipidosis.

A recent publication in Science has proposed that cationic amphiphilic drugs repurposed for COVID-19 typically use phosholipidosis as their antiviral mechanism of action in cells but will have no in vivo efficacy. On the contrary, our viewpoint, supported by additional experimental data for similar cationic amphiphilic drugs, indicates that many of these molecules have both in vitro and in vivo efficacy with no reported phospholipidosis, and therefore, this class of compounds should not be avoided but further explored, as we continue the search for broad spectrum antivirals.

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.