Grammatical evolution-based design of SARS-CoV-2 main protease inhibitors.

A series of SARS-CoV-2 main protease (SARS-CoV-2-M) inhibitors were modeled using evolutive grammar algorithms. We have generated an automated program that finds the best candidate to inhibit the main protease, M, 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.

Design of Spiking Central Pattern Generators for Multiple Locomotion Gaits in Hexapod Robots by Christiansen Grammar Evolution.

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.

Quadrupedal Robot Locomotion: A Biologically Inspired Approach and Its Hardware Implementation.

A bioinspired locomotion system for a quadruped robot is presented. Locomotion is achieved by a spiking neural network (SNN) that acts as a Central Pattern Generator (CPG) producing different locomotion patterns represented by their raster plots. To generate these patterns, the SNN is configured with specific parameters (synaptic weights and topologies), which were estimated by a metaheuristic method based on Christiansen Grammar Evolution (CGE).