Abstract
Indisputable importance of drug solubility in various industrial perspectives has moti-vated the scientists to evaluate different techniques to improve it. Fenoprofen is a significant non -steroidal anti-inflammatory drug (NSAID), that is the orally administered to relieve mild to moderate pain and the unfavorable symptoms of osteoarthritis and rheumatoid arthritis (i.e., inflammation and stiffness). Supercritical fluids (SCFs) belong to a certain type of fluids, in which their temperature and pressure are higher than the critical point. This property allows the CO2SCF to simultaneously possess the characteristics of both a liquid and a gas. The prominent target of this paper is to mathematically develop three predictive models via machine learning (ML) technique to optimize the solubility of Fenoprofen in CO2SCF. In this study, we have 32 data vectors in each dataset, including two input features of pressure and temperature. The output target is solubility, which we are going to model and analyze. Models are constructed through the use of Modular ANN (MANN), Gaussian processes regression (GPR), and the K-Nearest Neighbor technique (KNN) in this body of work. The glowworm swarm optimization (GSO) swarm-based method is utilized in order to carry out the process of model optimization. The root mean squared error (RMSE) rates for GSO-KNN, GSO-MANN, and GSO-GPR are respectively 5.25E-04, 5.46E-04, and 3.01E-05. The aforementioned models were also judged according to a number of other cri-teria, and since the GSO-GPR model was found to be the most effective according to all of these standards, it is being treated as the conclusive model of this investigation. In addition, the maximum error has been brought down to 5.02E-05 with the help of this model, which has an R2-score of 0.999.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of King Saud University. This is an open accessarticle under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).