Hybrid <i>In Silico</i> Approach Reveals Novel Inhibitors of Multiple SARS-CoV-2 Variants

Jain, Sankalp; Talley, Daniel C.; Baljinnyam, Bolormaa; Choe, Jun; Hanson, Quinlin; Zhu, Wei; Xu, Miao; Chen, Catherine Z.; Zheng, Wei; Hu, Xin; Shen, Min; Rai, Ganesha; Hall, Matthew D.; Simeonov, Anton; Zakharov, Alexey

doi:10.1021/acsptsci.1c00176

Cited by 9 publications

(7 citation statements)

References 51 publications

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“…For each of the clusters obtained from different cluster distance thresholds, MFP and SFP models were generated that incorporate the features of selected compounds per cluster. 58 A good pharmacophore model should not only be able to estimate the activity of active compounds but also have the ability to identify the active molecules from a database containing a large number of inactive compounds. To select the best models for screening, we applied these models on our complete dataset (training and test set combined) and calculated the percentage of active and inactive that hit these pharmacophore models.…”

Section: Methodsmentioning

confidence: 99%

“…To design the LBP models, the actives (from the training set) were clustered based on pharmacophore-based similarity (cluster distances 0.4, 0.6, 0.7, and 0.8, respectively). For each of the clusters obtained from different cluster distance thresholds, MFP and SFP models were generated that incorporate the features of selected compounds per cluster . A good pharmacophore model should not only be able to estimate the activity of active compounds but also have the ability to identify the active molecules from a database containing a large number of inactive compounds.…”

Section: Methodsmentioning

confidence: 99%

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Allosteric Binders of ACE2 Are Promising Anti-SARS-CoV-2 Agents

Hochuli

Jain

Melo-Filho

et al. 2022

ACS Pharmacol. Transl. Sci.

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The COVID-19 pandemic has had enormous health, economic, and social consequences. Vaccines have been successful in reducing rates of infection and hospitalization, but there is still a need for acute treatment of the disease. We investigate whether compounds that bind the human angiotensin-converting enzyme 2 (ACE2) protein can decrease SARS-CoV-2 replication without impacting ACE2’s natural enzymatic function. Initial screening of a diversity library resulted in hit compounds active in an ACE2-binding assay, which showed little inhibition of ACE2 enzymatic activity (116 actives, success rate ∼4%), suggesting they were allosteric binders. Subsequent application of in silico techniques boosted success rates to ∼14% and resulted in 73 novel confirmed ACE2 binders with K d values as low as 6 nM. A subsequent SARS-CoV-2 assay revealed that five of these compounds inhibit the viral life cycle in human cells. Further effort is required to completely elucidate the antiviral mechanism of these ACE2-binders, but they present a valuable starting point for both the development of acute treatments for COVID-19 and research into the host-directed therapy.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Allosteric Binders of ACE2 Are Promising Anti-SARS-CoV-2 Agents

Hochuli

Jain

Melo-Filho

et al. 2022

ACS Pharmacol. Transl. Sci.

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“…In our study, we complemented this modern approach by integrating it with quantitative structureactivity relationship (QSAR) modeling and quantitative high-throughput screening (qHTS). This combination allowed for a more systematic and data-informed identification of potential drug compounds [12,13]. Our methodology was further enriched by incorporating extensive chemical databases [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…These databases, replete with molecular building blocks and reaction templates offer a rich resource for data-driven drug design, facilitating the exploration of a wide range of chemical structures and properties [14]. QSAR modeling has been successfully used in the past for virtual screening, where it helps predict the biological activity of compounds based on their chemical structures [13,[17][18][19][20][21]. This prediction capability of QSAR models aids in narrowing down the vast pool of potential compounds to those most likely to exhibit desired biological activities [22].…”

Section: Introductionmentioning

confidence: 99%

AI-driven drug discovery: identification and optimization of ALDH3A1 selective inhibitors with nanomolar activity

Jain,

Yasgar,

Dalal

et al. 2024

Preprint

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In the field of medicinal chemistry, the discovery of novel compounds with therapeutic potential is of utmost importance. However, the conventional approach to drug discovery, relying on high-throughput screening (HTS), encounters limitations such as reagent availability and labor costs. Although a hit-finding campaign starts with a virtual screen of millions of compounds, the hit-to-lead and lead optimization stages often require designing, synthesizing, and profiling thousands of analogs before selecting clinical candidates. Recent advances offer an innovative solution - the virtual generation of billions of synthetically feasible compounds with an impressive 80% success rate for synthesis. This breakthrough has the potential to significantly expand the chemical space available for purchase and experimental validation, bringing about a revolution in the field. Our study presents a comprehensive approach to compound discovery and optimization, harnessing quantitative high-throughput screening (qHTS), chemical databases, and reaction-based enumeration. To further enhance the synthesis process and gain deeper insights into compound formation, we utilize the Reaction Cookbook from Biosolveit, which comprises reaction SMARTs for around 300 chemical reactions. By employing this wide range of reactions, we aim to uncover the full spectrum of a chemotype's potential and customize its structure to optimize desired properties. As an illustration of this approach, our work on the ALDH3A1 project resulted in the synthesis of 50 compounds, with 21 of them exhibiting activity (negative curve class values; hit-rate ~42%). Among these active compounds, 6 displayed IC50 values lower than 30 µM and efficacy values less than -50%, with the most potent compound achieving an impressive potency of 447 nM. This study demonstrates the successful synergy between in-silico reaction-based analogs enumeration, molecular modeling and AI/ML-based techniques in identifying compounds with improved biological activity, offering promising prospects for the development of ALDH3A1-targeting agents as potential cancer therapeutics. Computational workflows developed in this study can be used for similar target-based drug discovery campaigns.

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“…The author highlights that the trimeric spike (S) protein appears to be optimal in the sense of inducing an immunity that leaves the virus with no evolutionary route of evasion . Jain et al develop a hybrid in silico approach utilizing screening data from the National Center for Advancing Translational Sciences (NCATS) to build novel inhibitors of multiple SARS-CoV-2 variants using both machine learning and pharmacophore-based modeling …”

mentioning

confidence: 99%

Chronic Lung and Respiratory Conditions Affecting Lungs and Airways

Schneider‐Futschik

2022

ACS Pharmacol. Transl. Sci.

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Hybrid In Silico Approach Reveals Novel Inhibitors of Multiple SARS-CoV-2 Variants

Cited by 9 publications

References 51 publications

Allosteric Binders of ACE2 Are Promising Anti-SARS-CoV-2 Agents

Allosteric Binders of ACE2 Are Promising Anti-SARS-CoV-2 Agents

AI-driven drug discovery: identification and optimization of ALDH3A1 selective inhibitors with nanomolar activity

Chronic Lung and Respiratory Conditions Affecting Lungs and Airways

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