Antagonistic Trends Between Binding Affinity and Drug-Likeness in SARS-CoV-2 Mpro Inhibitors Revealed by Machine Learning
The SARS-CoV-2 main protease (Mpro) is a validated therapeutic target for inhibiting viral replication. Few compounds have advanced clinically, underscoring the difficulty in optimizing both target affinity and drug-like properties. To address this challenge, we integrated machine learning (ML), mol...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Viruses |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1999-4915/17/7/935 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849713325031555072 |
|---|---|
| author | Anacleto Silva de Souza Vitor Martins de Freitas Amorim Eduardo Pereira Soares Robson Francisco de Souza Cristiane Rodrigues Guzzo |
| author_facet | Anacleto Silva de Souza Vitor Martins de Freitas Amorim Eduardo Pereira Soares Robson Francisco de Souza Cristiane Rodrigues Guzzo |
| author_sort | Anacleto Silva de Souza |
| collection | DOAJ |
| description | The SARS-CoV-2 main protease (Mpro) is a validated therapeutic target for inhibiting viral replication. Few compounds have advanced clinically, underscoring the difficulty in optimizing both target affinity and drug-like properties. To address this challenge, we integrated machine learning (ML), molecular docking, and molecular dynamics (MD) simulations to investigate the balance between pharmacodynamic (PD) and pharmacokinetic (PK) properties in Mpro inhibitor design. We developed ML models to classify Mpro inhibitors based on experimental IC<sub>50</sub> data, combining molecular descriptors with structural insights from MD simulations. Our Support Vector Machine (SVM) model achieved strong performance (training accuracy = 0.84, ROC AUC = 0.91; test accuracy = 0.79, ROC AUC = 0.86), while our Logistic Regression model (training accuracy = 0.78, ROC AUC = 0.85; test accuracy = 0.76, ROC AUC = 0.83). Notably, PK descriptors often exhibited opposing trends to binding affinity: hydrophilic features enhanced binding affinity but compromised PK properties, whereas hydrogen bonding, hydrophobic, and π–π interactions in Mpro subsites S2 and S3/S4 are fundamental for binding affinity. Our findings highlight the need for a balanced approach in Mpro inhibitor design, strategically targeting these subsites may balance PD and PK properties. For the first time, we demonstrate antagonistic trends between pharmacokinetic (PK) and pharmacodynamic (PD) features through the integrated application of ML/MD. This study provides a computational framework for rational Mpro inhibitors, combining ML and MD to investigate the complex interplay between enzyme inhibition and drug likeness. These insights may guide the hit-to-lead optimization of the novel next-generation Mpro inhibitors of SARS-CoV-2 with preclinical and clinical potential. |
| format | Article |
| id | doaj-art-e89817838d204c0287ac27ddc33499e0 |
| institution | DOAJ |
| issn | 1999-4915 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Viruses |
| spelling | doaj-art-e89817838d204c0287ac27ddc33499e02025-08-20T03:13:59ZengMDPI AGViruses1999-49152025-06-0117793510.3390/v17070935Antagonistic Trends Between Binding Affinity and Drug-Likeness in SARS-CoV-2 Mpro Inhibitors Revealed by Machine LearningAnacleto Silva de Souza0Vitor Martins de Freitas Amorim1Eduardo Pereira Soares2Robson Francisco de Souza3Cristiane Rodrigues Guzzo4Department of Microbiology, Institute of Biomedical Sciences, University of São Paulo, Sao Paulo 05508-000, BrazilDepartment of Microbiology, Institute of Biomedical Sciences, University of São Paulo, Sao Paulo 05508-000, BrazilDepartment of Microbiology, Institute of Biomedical Sciences, University of São Paulo, Sao Paulo 05508-000, BrazilDepartment of Microbiology, Institute of Biomedical Sciences, University of São Paulo, Sao Paulo 05508-000, BrazilDepartment of Microbiology, Institute of Biomedical Sciences, University of São Paulo, Sao Paulo 05508-000, BrazilThe SARS-CoV-2 main protease (Mpro) is a validated therapeutic target for inhibiting viral replication. Few compounds have advanced clinically, underscoring the difficulty in optimizing both target affinity and drug-like properties. To address this challenge, we integrated machine learning (ML), molecular docking, and molecular dynamics (MD) simulations to investigate the balance between pharmacodynamic (PD) and pharmacokinetic (PK) properties in Mpro inhibitor design. We developed ML models to classify Mpro inhibitors based on experimental IC<sub>50</sub> data, combining molecular descriptors with structural insights from MD simulations. Our Support Vector Machine (SVM) model achieved strong performance (training accuracy = 0.84, ROC AUC = 0.91; test accuracy = 0.79, ROC AUC = 0.86), while our Logistic Regression model (training accuracy = 0.78, ROC AUC = 0.85; test accuracy = 0.76, ROC AUC = 0.83). Notably, PK descriptors often exhibited opposing trends to binding affinity: hydrophilic features enhanced binding affinity but compromised PK properties, whereas hydrogen bonding, hydrophobic, and π–π interactions in Mpro subsites S2 and S3/S4 are fundamental for binding affinity. Our findings highlight the need for a balanced approach in Mpro inhibitor design, strategically targeting these subsites may balance PD and PK properties. For the first time, we demonstrate antagonistic trends between pharmacokinetic (PK) and pharmacodynamic (PD) features through the integrated application of ML/MD. This study provides a computational framework for rational Mpro inhibitors, combining ML and MD to investigate the complex interplay between enzyme inhibition and drug likeness. These insights may guide the hit-to-lead optimization of the novel next-generation Mpro inhibitors of SARS-CoV-2 with preclinical and clinical potential.https://www.mdpi.com/1999-4915/17/7/935drug discoveryhit-to-lead optimization challengesmain proteaseSARS-CoV-2machine learningmolecular dynamics simulations |
| spellingShingle | Anacleto Silva de Souza Vitor Martins de Freitas Amorim Eduardo Pereira Soares Robson Francisco de Souza Cristiane Rodrigues Guzzo Antagonistic Trends Between Binding Affinity and Drug-Likeness in SARS-CoV-2 Mpro Inhibitors Revealed by Machine Learning Viruses drug discovery hit-to-lead optimization challenges main protease SARS-CoV-2 machine learning molecular dynamics simulations |
| title | Antagonistic Trends Between Binding Affinity and Drug-Likeness in SARS-CoV-2 Mpro Inhibitors Revealed by Machine Learning |
| title_full | Antagonistic Trends Between Binding Affinity and Drug-Likeness in SARS-CoV-2 Mpro Inhibitors Revealed by Machine Learning |
| title_fullStr | Antagonistic Trends Between Binding Affinity and Drug-Likeness in SARS-CoV-2 Mpro Inhibitors Revealed by Machine Learning |
| title_full_unstemmed | Antagonistic Trends Between Binding Affinity and Drug-Likeness in SARS-CoV-2 Mpro Inhibitors Revealed by Machine Learning |
| title_short | Antagonistic Trends Between Binding Affinity and Drug-Likeness in SARS-CoV-2 Mpro Inhibitors Revealed by Machine Learning |
| title_sort | antagonistic trends between binding affinity and drug likeness in sars cov 2 mpro inhibitors revealed by machine learning |
| topic | drug discovery hit-to-lead optimization challenges main protease SARS-CoV-2 machine learning molecular dynamics simulations |
| url | https://www.mdpi.com/1999-4915/17/7/935 |
| work_keys_str_mv | AT anacletosilvadesouza antagonistictrendsbetweenbindingaffinityanddruglikenessinsarscov2mproinhibitorsrevealedbymachinelearning AT vitormartinsdefreitasamorim antagonistictrendsbetweenbindingaffinityanddruglikenessinsarscov2mproinhibitorsrevealedbymachinelearning AT eduardopereirasoares antagonistictrendsbetweenbindingaffinityanddruglikenessinsarscov2mproinhibitorsrevealedbymachinelearning AT robsonfranciscodesouza antagonistictrendsbetweenbindingaffinityanddruglikenessinsarscov2mproinhibitorsrevealedbymachinelearning AT cristianerodriguesguzzo antagonistictrendsbetweenbindingaffinityanddruglikenessinsarscov2mproinhibitorsrevealedbymachinelearning |