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Varikoti RA, Schultz KJ, Kombala CJ, Kruel A, Brandvold KR, Zhou M, Kumar N. Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV-2 main protease. J Comput Aided Mol Des 2023:10.1007/s10822-023-00509-1. [PMID: 37314632 DOI: 10.1007/s10822-023-00509-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 05/23/2023] [Indexed: 06/15/2023]
Abstract
Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) models followed by experimental validation in an iterative manner. Generative deep learning models can generate thousands of new candidates, however, their physiochemical and biochemical properties are typically not fully optimized. Using our recently developed deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 Mpro that preserve the core scaffold. We utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure-activity relationships, multi-parameter optimization, and graph neural networks on generated candidates to predict biological activity and binding affinity in advance. As a result of these combined computational endeavors, eight promising candidates were singled out and put through experimental testing using Native Mass Spectrometry and FRET-based functional assays. Two of the tested compounds with quinazoline-2-thiol and acetylpiperidine core moieties showed IC[Formula: see text] values in the low micromolar range: [Formula: see text] [Formula: see text]M and 3.41±0.0015 [Formula: see text]M, respectively. Molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations within the chain B and the interface domains of the Mpro. Our integrated approach provides a platform for data driven lead optimization with rapid characterization and experimental validation in a closed loop that could be applied to other potential protein targets.
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Affiliation(s)
- Rohith Anand Varikoti
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99352, USA
| | - Katherine J Schultz
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99352, USA
| | - Chathuri J Kombala
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99352, USA
| | - Agustin Kruel
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99352, USA
| | - Kristoffer R Brandvold
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99352, USA
| | - Mowei Zhou
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99352, USA
| | - Neeraj Kumar
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99352, USA.
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Kim JH, Kim H, Kim WY. Effect of molecular representation on deep learning performance for prediction of molecular electronic properties. B KOREAN CHEM SOC 2022. [DOI: 10.1002/bkcs.12516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Jun Hyeong Kim
- Department of Chemistry Korea Advanced Institute of Science and Technology Daejeon South Korea
| | - Hyeonsu Kim
- Department of Chemistry Korea Advanced Institute of Science and Technology Daejeon South Korea
| | - Woo Youn Kim
- Department of Chemistry Korea Advanced Institute of Science and Technology Daejeon South Korea
- KI for Artificial Intelligence Korea Advanced Institute of Science and Technology Daejeon South Korea
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Kang B, Seok C, Lee J. A benchmark study of machine learning methods for molecular electronic transition: Tree‐based ensemble learning versus graph neural network. B KOREAN CHEM SOC 2022. [DOI: 10.1002/bkcs.12468] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Beomchang Kang
- Department of Chemistry Seoul National University Seoul South Korea
| | - Chaok Seok
- Department of Chemistry Seoul National University Seoul South Korea
| | - Juyong Lee
- Department of Chemistry, Division of Chemistry and Biochemistry Kangwon National University Chuncheon South Korea
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