The search for new efficient inhibitors of SARS-COV-2 through the De novo drug design developed by artificial intelligence

Abstract

The pandemic caused by Sars-CoV-2 is a viral infection that has generated one of the most significant health problems worldwide. Previous studies report the main protease (Mpro) as a potential target for this virus, as it is considered a crucial enzyme in mediating replication and viral transcription. This work presented the construction of new bioactive compounds for possible inhibition. The De novo molecular design of drugs method in the incremental construction of a ligant model within a receptor model was used, producing new structures with the help of artificial intelligence. The research algorithm and the scoring function responsible for predicting orientation and affinity in the molecular target at the time of coupling showed, as a result of the simulation, the compound with the highest bioaffinity value, Hit 998, with the energy of −17.62 kcal/mol, and synthetic viability close to 50%. While hit 1103 presented better synthetic viability (80%), its affinity energy of −10.28 kcal/mol. Both were compared with the reference linker N3, with a binding affinity of −7.5 kcal/mol. ADMET tests demonstrated that simulated compounds have a low risk of metabolic activation and do not exert effective distribution in the CNS, suggesting a pharmacokinetic mechanism based on local action, even with high topological polarity, which resulted in low oral bioavailability. In conclusion, MMGBSA, H-bonds, RMSD, SASA, and RMSF values were also obtained through molecular dynamics to verify the stability of the receptor-ligant complex within the active protein site to seek new therapeutic propositions in the fight against the pandemic.

Communicated by Ramaswamy H. Sarma

Keywords:

• Main protease
• covid19
• molecular docking
• pandemic
• deep learning

Acknowledgements

The authors thank the Ceará Foundation for Support to Scientific and Technological Development-FUNCAP, the State University of Ceara for the support.

Disclosure statement

The authors declare that they have no conflict of interest.

Ethics approval

The datasets and the work do not contain personal or sensitive information, no ethical issue is concerned.

Author contributions

All the authors have contributed to this work in an appropriate way.

Consent for publication

The authors are fine that the work (including all content, data and images) is published by Machine Learning Journal.

Consent to participate

The authors are fine that the work is submitted and published by Machine Learning Journal. There is no human study in this work, so this aspect is not applicable.

Additional information

Funding

Hélcio Silva dos Santos acknowledges fiancial support from the PQ-BPI/FUNCAP (Grant#: BP4-0172-00075.01.00/20) and Carla Freire Celedonio Fernandes acknowledges financial support received from project Inova Fiocruz FUNCAP (Grant#:06481104-2020).