Grammatical evolution-based design of nucleotic analogs for SARS-CoV-2's replication-transcription complex

Area of Science:

• Computational chemistry
• Drug discovery
• Molecular modeling

Background:

• Drug design increasingly utilizes in silico tools for identifying novel therapeutic agents.
• Structure-based drug design is crucial for targeting specific enzymes like SARS-CoV-2 RNA-dependent RNA polymerase.

Purpose of the Study:

• To design novel molecules capable of inhibiting the SARS-CoV-2 RNA-dependent RNA polymerase using a Grammatical Evolution approach.
• To explore a defined chemical space for potential drug candidates.

Main Methods:

• Utilized Grammatical Evolution to explore chemical space defined by a pharmacophore model.
• Generated 1D molecular strings, converted them to 3D structures, and performed automated docking calculations.
• Conducted 200-nanosecond molecular dynamics simulations for stability analysis.

Main Results:

• Designed four novel molecules that inhibit SARS-CoV-2 RNA-dependent RNA polymerase.
• Achieved ligand efficiencies and pharmacokinetic profiles comparable to Remdesivir.
• Two molecules demonstrated stable interactions with the target enzyme through weak hydrogen bonds in molecular dynamics simulations.

Conclusions:

• Grammatical Evolution is an effective strategy for de novo drug design against viral targets.
• The identified molecules represent promising leads for COVID-19 therapeutics.
• The developed software is publicly available for further research.