Optimization of Drug Design Composition by Hybrid Islamic and Evolutionary Medicine for Covid-19 and Its New Variants Using Geometric Time Variants Extreme Genetic Algorithm
Imam Cholissodin, Lailil Muflikhah, Sutrisno Sutrisno, Arief Andy Soebroto, Aurick Yudha Nagara, Renny Nova, Tamara Gusti Ebtavanny, Zanna Annisa Nur Azizah Fareza · Advances in engineering research/Advances in Engineering Research · 2023
There is a difficulty in building the implementation of a computational model to build a complex Covid-19 drug design involving a smart ecosystem.Covid-19 and the drug design of its new variants are formed by combining the appropriate compound and dose as an antiviral.Drug designs as the candidates for Covid-19 drugs can be in the form of herbal medicines and other materials.In computing the design of this drug, the encountered problem is the way to separate the features between the mixed compounds.The feature extraction received will be optimized into compounds that are useful as Covid-19 drug candidates.On the other hand, drug design using manual computational methods is very complicated and requires a fairly long-time estimation in forming the proper compound with many variants of each compound.From the problems that occur, it requires a system that can perform drug design computations quickly and precisely.Therefore, a new method of combining extreme learning machines and genetic algorithms is made called Geometric Time Variants (GTV) Extreme Genetic Algorithm (XtremeGA or eXGA or ExGA).As a result, drug design optimization using historical data by hybrid Islamic and evolutionary medicine for Covid-19 and its new variants can work quickly, optimally, and achieved convergence conditions.