Recent innovations have focused on the creation of new families that extend well-known distributions while providing a huge amount of practical flexibility for data modeling. Weighted distributions offer an effective approach for addressing model building and data interpretation problems. The main objective of this work is to provide a novel family based on a weighted generator called the length-biased truncated Lomax-generated (LBTLo-G) family. Discussions are held about the characteristics of the LBTLo-G family, including expressions for the probability density function, moments, and incomplete moments. In addition, different measures of uncertainty are determined. We provide four new sub-distributions and investigated their functionalities. Subsequently, a statistical analysis is given. The LBTLo-G family's parameter estimation is carried out using the maximum likelihood technique on the basis of full and censored samples. Simulation research is conducted to determine the parameters of the LBTLo Weibull (LBTLoW) distribution. Four genuine data sets are considered to illustrate the fitting behavior of the LBTLoW distribution. In each case, the application outcomes demonstrate that the LBTLoW distribution can, in fact, fit the data more accurately than other rival distributions.

This research explores how Generative Artificial Intelligence (AI) can be used to accelerate drug discovery, especially in developing nations like Nigeria. By integrating various generative models; including GANs, VAEs, and Transformer-based architectures—the study aims to rapidly create new molecular structures with therapeutic potential. A unique aspect of this research is its use of local Nigerian resources, such as indigenous medicinal plants and traditional knowledge, to create a specialized dataset. By combining this local data with global molecular databases, the framework is designed to find candidate molecules with better drug-likeness, lower toxicity, and higher binding affinity to target proteins. This approach not only speeds up the preclinical phase of drug discovery but also promotes sustainable healthcare innovation by utilizing Nigeria’s own resources. The study highlights its potential application in finding treatments for malaria, sickle cell disease, and antimicrobial resistance—all major health concerns in Nigeria.

The length biased truncated Lomax distribution is introduced in this study as a weighted form of the truncated Lomax distribution. The length biased truncated Lomax distribution’s essential distributional features are investigated. In the case of complete and type-II censored data, the maximum likelihood method is provided for estimating population parameter. The model parameter asymptotic confidence interval is calculated. To demonstrate the pattern of the estimate, a sample generation algorithm is supplied, as well as a Monte Carlo simulation analysis. We can see from the simulation research that as the censoring level is increased, the mean squared error of parameter estimates decrease’s for all given values. With increasing sample size, the mean squared error and average length of parameter estimates decrease. The estimates get increasingly accurate as the sample size grows higher, suggesting that its asymptotically unbiased. Furthermore, in all cases, the mean squared error diminishes as the sample size grows, indicating that the estimates of parameter are consistent. Modelling to medical data and the percentage of household spending on education out of total household expenditure from the household income, expenditure and consumption survey (HIECS) data are used to show the importance of the new model. The Kumaraswamy, beta, truncated power Lomax, truncated Weibull, and one parameter-beta distributions perform poorly in comparison to the suggested distribution.