Schizophrenia is a chronic psychotic disorder characterized primarily by cognitive deficits. Drugs and therapies are helpful in managing the symptoms, mostly with long-term compliance. There is a pressing need to design more efficient drugs with fewer adverse effects. Solubility, metabolic stability, toxicity, permeability, and transporter effects are important parameters in the efficacy of drug design, which in turn depend upon different physical and chemical characteristics of drugs. In recent years, there has been growing interest in developing computational tools for the discovery and development of drugs for schizophrenia. Some of these methods use machine learning algorithms to predict the efficacy and side effects of the potential drugs. Other studies have used computer simulations to understand the molecular mechanisms underlying the disease and identify new targets for drug development. Topological indices are numeric quantities linked to the chemical structure of drugs and predict the properties, reactivity, and stability of drugs through the quantitative structure-property relationship (QSPR). This work is aimed at using statistical techniques to link QSPR correlating properties with connectivity indices using linear regression. The QSPR model gives quite a better estimation of the properties of drugs, such as melting point, boiling point, enthalpy, flash point, molar refractivity, refractive index, complexity, molecular weight, and refractivity. Results are validated by comparing actual values to estimated values for the drugs.

Micro/nanoscale fabricated devices have widely been used in modern technology and bioengineering as they offer excellent heat transfer. Removal of excess heat, coolant selection, rapid mixing, and handling proportion of colloidal metallic nanogranules in the base fluid are the main challenges in micro/nanofluidic systems. To address these problems, the primary motivation of the intended mathematical flow problem is to investigate the thermal and flow aspects of blood-based ternary nanofluid in the presence of inclined magnetic field and thermal radiations through a microfluidic pump with elastic walls. Further, the pump inner surface is smeared with fabricated cilia. The embedded cilia blow in coordination to start metachronal travelling waves along the pump wall that assist homogenous mixing and manipulation. The entire analysis is conducted in moving frame and simplified under lubrication and Rosseland approximations. Numerical solution of various flow and thermal entities are computed via the shooting method and plotted for different values of the parameters of interest. A comparative glimpse allows us to conclude that the trimetallic blood-based nanofluid exhibits elevated heat transfer rate by 12–18%, bi-metallic by about 11–12%, and mono nanofluid by about 6% compared to the conventional blood model. The study also determines that the prolonged cilia commence augmentation in flowrate and pressure-gradient around the pump deep portion. Furthermore, the radiated ternary liquid under fragile magnetic field effects may contribute to the cooling process by eliminating unnecessary heat from the system. It is also noticed that around the ciliated wall, the heat transfer irreversibility effects are appreciable over the fluid frictional irreversibility.

A topological index is a score function that changes each molecular structure to a unique real number. It aids in determining the physicochemical and biological properties of a wide range of drugs, and it more accurately reflects the theoretical properties of drugs. The task is accomplished through the use of degree-based topological indices (TIs). Heart disease refers to a group of disorders that affect our hearts. Heart diseases include blood vessel disorders such as irregular heartbeat and heart muscle disease. In this article, Eliquis, metoprolol, valsartan, etc., and other drugs employed to treat cardiovascular disease are studied and the QSPR analysis goal will probe the mathematical relationship of properties such as (polarity, boiling point, enthalpy, etc.) and assorted descriptors associated to drug’s structure. The present study on TIs imposed on drugs was found to have a good correlation with physical properties.

Multiple sclerosis (MS) is a complex autoimmune disease of the central nervous system with an unknown etiology. While disease-modifying therapies can slow progression, there is a need for more effective treatments. Quantitative structure-activity relationship (QSAR) modeling using topological indices derived from chemical graph theory is a promising approach to rationally design new drugs for MS. Using a linear regression approach, we create models for Quantitative Structure-Property Relations (QSPR), detecting correlations between properties such as enthalpy of vaporization, flash point, molar weight, polarizability, molar volume, and complexity with certain degree related topological indices. We used a dataset related to drugs for MS with known properties for training the model and also for validation. To prioritize the most promising drug candidates, we used multi-criteria decision making based on the predicted properties and topological indices, allowing for more informed decisions. The 12 drug candidates were prioritized using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and two Weighted Aggregated Sum Product Assessment (WASPAS) methods. The rankings obtained using TOPSIS, WASPAS methods showed a high level of agreement among the results. This framework can be broadly applied to rationally design new therapeutics for complex diseases.

Every year, various experiments emerge in which a strong link between topological chemical structures and their properties is found. These properties are numerous such as melting point, boiling point, and drug toxicity. Topological index is the functional tool to determine these properties. This research paper will analyze some of the molecular drug structures, i.e., hyaluronic acid‐paclitaxel conjugates G_n, anticancer drug SP[n], polyomino chain of n‐cycle Z_n, triangular benzenoid T_n, and circumcoronene benzenoid series H_k using multicriteria decision‐making techniques including TOPSIS and SAW. The topological indices used in this research paper include the Randić index for α = 1, −1, 1/2, the augmented Zagreb index and the forgotten topological index.