RESUMEN
A series of SARS-CoV-2 main protease (SARS-CoV-2-Mpro) inhibitors were modeled using evolutive grammar algorithms. We have generated an automated program that finds the best candidate to inhibit the main protease, Mpro, of SARS-CoV-2. The candidates were constructed based on a pharmacophore model of the above-mentioned target; relevant moieties of such molecules were modified using data-basis sets with similar chemical behavior to the reference moieties. Additionally, we used the SMILES language to translate 3D chemical structures to 1D words; then, an evolutive grammar algorithm was used to explore the chemical space and obtain new candidates, which were evaluated via the binding energy of molecular coupling assays as an evaluation function. Finally, sixteen molecules were obtained in 3 runs of our program, three of which show promising binding properties as SARS-CoV-2-Mpro inhibitors. One of them, TTO, maintained its relevant binding properties during 100 ns molecular dynamics experiments. For this reason, TTO is the best candidate to inhibit SARS-CoV-2-Mpro. The software we developed for this contribution is available at the following URL: https://github.com/masotelof/GEMolecularDesign.
Asunto(s)
COVID-19 , Inhibidores de Proteasas , Proteasas 3C de Coronavirus , Humanos , Simulación del Acoplamiento Molecular , Simulación de Dinámica Molecular , Inhibidores de Proteasas/química , SARS-CoV-2RESUMEN
This paper presents a method to design Spiking Central Pattern Generators (SCPGs) to achieve locomotion at different frequencies on legged robots. It is validated through embedding its designs into a Field-Programmable Gate Array (FPGA) and implemented on a real hexapod robot. The SCPGs are automatically designed by means of a Christiansen Grammar Evolution (CGE)-based methodology. The CGE performs a solution for the configuration (synaptic weights and connections) for each neuron in the SCPG. This is carried out through the indirect representation of candidate solutions that evolve to replicate a specific spike train according to a locomotion pattern (gait) by measuring the similarity between the spike trains and the SPIKE distance to lead the search to a correct configuration. By using this evolutionary approach, several SCPG design specifications can be explicitly added into the SPIKE distance-based fitness function, such as looking for Spiking Neural Networks (SNNs) with minimal connectivity or a Central Pattern Generator (CPG) able to generate different locomotion gaits only by changing the initial input stimuli. The SCPG designs have been successfully implemented on a Spartan 6 FPGA board and a real time validation on a 12 Degrees Of Freedom (DOFs) hexapod robot is presented.