Discovery of Potent Covid-19 Main Protease Inhibitors Using Machine Learning Based Virtual Screening Strategy
The emergence and rapid spreading of novel SARS-CoV-2 across the globe represent an imminent threat to public health. Novel antiviral therapies are urgently needed to overcome this pandemic. Given the great role of main protease of Covid-19 for virus replication, we performed drug repurposing study using recently deposited main protease structure, 6LU7. For instance, pharmacophore- and e-pharmacophore-based hypotheses such as AARRH and AARR respectively were developed using available small molecule inhibitors and utilized in the screening of DrugBank repository. Further, hierarchical docking protocol was implemented with the support of Glide algorithm. The resultant compounds were then examined for its binding free energy against main protease of Covid-19 by means of Prime-MM/GBSA algorithm. Most importantly, the resultant compounds antiviral activities were further predicted by machine learning-based model generated by AutoQSAR algorithm. Finally, the hit molecules were examined for its drug likeness and its toxicity parameters through QikProp algorithm. Overall, the present analysis yielded four potential inhibitors (DB07800, DB08573, DB03744 and DB02986) that are predicted to bind with main protease of Covid-19 better than currently used drug molecules such as N3 (co-crystallized native ligand), Lopinavir and Ritonavir..
| Medienart: |
|
|---|
Preprint| Erscheinungsjahr: |
2021 |
|---|---|
| Erschienen: |
2021 |
| Enthalten in: |
chemRxiv.org - (2021) vom: 17. Nov. Zur Gesamtaufnahme - year:2021 |
|---|
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
T, Muthu Kumar [VerfasserIn] |
|---|
| Links: |
Volltext [kostenfrei] |
|---|
| Themen: |
|---|
| doi: |
10.26434/chemrxiv.12090423 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
XCH017603870 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | XCH017603870 | ||
| 003 | DE-627 | ||
| 005 | 20230429143340.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 200420s2021 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.26434/chemrxiv.12090423 |2 doi | |
| 035 | |a (DE-627)XCH017603870 | ||
| 035 | |a (chemrXiv)10.26434/chemrxiv.12090423 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a T, Muthu Kumar |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Discovery of Potent Covid-19 Main Protease Inhibitors Using Machine Learning Based Virtual Screening Strategy |
| 264 | 1 | |c 2021 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a Computermedien |b c |2 rdamedia | ||
| 338 | |a Online-Ressource |b cr |2 rdacarrier | ||
| 520 | |a The emergence and rapid spreading of novel SARS-CoV-2 across the globe represent an imminent threat to public health. Novel antiviral therapies are urgently needed to overcome this pandemic. Given the great role of main protease of Covid-19 for virus replication, we performed drug repurposing study using recently deposited main protease structure, 6LU7. For instance, pharmacophore- and e-pharmacophore-based hypotheses such as AARRH and AARR respectively were developed using available small molecule inhibitors and utilized in the screening of DrugBank repository. Further, hierarchical docking protocol was implemented with the support of Glide algorithm. The resultant compounds were then examined for its binding free energy against main protease of Covid-19 by means of Prime-MM/GBSA algorithm. Most importantly, the resultant compounds antiviral activities were further predicted by machine learning-based model generated by AutoQSAR algorithm. Finally, the hit molecules were examined for its drug likeness and its toxicity parameters through QikProp algorithm. Overall, the present analysis yielded four potential inhibitors (DB07800, DB08573, DB03744 and DB02986) that are predicted to bind with main protease of Covid-19 better than currently used drug molecules such as N3 (co-crystallized native ligand), Lopinavir and Ritonavir. | ||
| 650 | 4 | |a Chemistry |7 (dpeaa)DE-84 | |
| 650 | 4 | |a 540 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a K, Rohini |e verfasserin |4 aut | |
| 700 | 1 | |a James, Nivya |e verfasserin |4 aut | |
| 700 | 1 | |a V, Shanthi |e verfasserin |4 aut | |
| 700 | 1 | |a Karuppasamy, Ramanathan |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t chemRxiv.org |g (2021) vom: 17. Nov. |
| 773 | 1 | 8 | |g year:2021 |g day:17 |g month:11 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.26434/chemrxiv.12090423 |z kostenfrei |3 Volltext |
| 912 | |a GBV_XCH | ||
| 912 | |a SSG-OLC-PHA | ||
| 951 | |a AR | ||
| 952 | |j 2021 |b 17 |c 11 | ||