Dengue fever, caused by dengue virus (DENV), has become a serious threat to human lives. Phytochemicals are known to have great potential to eradicate viral, bacterial and fungal-borne diseases in human beings. This study was aimed at drug development against nonstructural proteins of dengue virus.

Epstein-Barr virus (EBV) plays a key role in the development of some tumor diseases. Therefore, this study intends to take a practical step in controlling the pathogenicity of this virus by designing an effective vaccine based on the Capsid Envelope and Epstein–Barr nuclear immunogen Proteins Epitopes. Currently, there are no effective drugs or vaccines to treat or prevent EBV infection.

FGFR-2 in cancers driven by its overexpression. Targeting FGFR-2 with specific inhibitors can disrupt uncontrolled cell growth and proliferation but off-target effects need to be addressed. Continued research is essential to optimize FGFR-2-targeted therapies and improve outcomes for patients with FGFR-2-driven cancers.

Cholinesterase inhibition can lead to adverse effects on humans and other organisms, as cholinesterase is essential for human and animal nervous system function. To develop non-harmful pesticides, targeting alternative mechanisms of action that do not involve cholinesterase inhibition is necessary. 

This project investigates the photovoltaic potential of chlorophyll c derivatives using quantum mechanical methods. By analyzing their electronic, optical, and charge transfer properties via DFT and TD-DFT, the study aims to identify bioinspired donor materials and sensitizers suitable for organic and dye-sensitized solar cells, promoting sustainable solar energy solutions. 

This project focuses on identifying and screening antiviral peptides as potential therapeutics against Dengue virus (DENV). Using computational modeling, molecular docking, and dynamics simulations, candidate peptides will be evaluated for their binding affinity, stability, and inhibitory potential, aiming to develop targeted, peptide-based treatments for dengue infection. 

This project focuses on the identification and evaluation of potential small-molecule inhibitors targeting the XYZ receptor, a key player in the development and progression of prostate cancer. Utilizing advanced computational techniques, including molecular docking, molecular dynamics simulations, and ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) analysis, the study aims to screen and optimize lead compounds. The goal is to discover effective and selective inhibitors that could serve as promising candidates for future therapeutic development in prostate cancer treatment. 

This project aims to perform high-throughput virtual and experimental screening of a diverse compound library to identify potent inhibitors against a major therapeutic target involved in disease progression. Using computational techniques such as molecular docking, pharmacokinetics analysis, and molecular dynamics simulations, top candidates will be prioritized for biological evaluation. The study seeks to accelerate drug discovery by identifying lead molecules with high specificity and efficacy, contributing to the development of targeted therapies for improved disease management. 

This project focuses on designing and evaluating novel metal-coordinated analogues of lisinopril to inhibit human angiotensin-converting enzyme (ACE), a key target in the treatment of cardiovascular diseases. Using a chemical analogue-based approach, the study employs molecular modeling, docking, and quantum chemical calculations to assess binding affinity and stability. The aim is to enhance ACE inhibition through metal coordination, offering a promising strategy for the development of next-generation antihypertensive agents with improved potency and selectivity. 

This project aims to design and optimize Olaparib analogues as selective PARP-1 inhibitors for treating BRCA-mutated triple-negative breast cancer (TNBC). Through computational drug design, molecular docking, dynamics simulations, and cheminformatics profiling (including ADMET and drug-likeness analysis), promising analogues will be identified. The study focuses on enhancing selectivity, potency, and pharmacokinetic properties to overcome resistance and toxicity issues. The goal is to support the development of next-generation targeted therapies for aggressive TNBC subtypes with BRCA mutations. 

This project aims to design a multi-epitope subunit vaccine targeting both HSV type 2 and HIV using an integrated in silico approach. Computational tools will be employed for epitope prediction, antigenicity and allergenicity assessment, population coverage analysis, and structural modeling. Molecular docking and immune simulation will evaluate vaccine–receptor interactions and immune responses. The goal is to develop a broad-spectrum, safe, and immunogenic vaccine candidate offering cross-protection against these two sexually transmitted viral infections. 

This project focuses on repurposing a PI3Kα-selective inhibitor for breast cancer therapy using a structure-guided approach. Key methodologies include pharmacophore modeling, molecular docking, and molecular dynamics simulations to evaluate binding affinity, selectivity, and stability of inhibitor-target interactions. The study aims to optimize known inhibitors for enhanced efficacy against PI3Kα, a critical oncogenic target in breast cancer. This integrated computational strategy supports accelerated drug repositioning and rational design of targeted therapies for effective breast cancer treatment. 

Structure-based drug design offers a powerful strategy for developing targeted therapeutics against Leishmania donovani, the causative agent of visceral leishmaniasis. This approach focuses on identifying essential parasite proteins, determining their 3D structures through crystallography or computational modeling, and virtually screening small molecules to predict high-affinity inhibitors. By analyzing active-site interactions, binding conformations, and pharmacokinetic properties, promising lead compounds can be optimized for potency and selectivity. Structure-guided refinement minimizes toxicity and accelerates the discovery of novel anti-leishmanial agents. This project aims to apply molecular docking, dynamics simulation, and ADMET profiling to design effective drug candidates against L. donovani.

Multi-epitope vaccine design integrates computational immunology and structural biology to develop safe, targeted, and highly immunogenic vaccine constructs. This approach involves predicting cytotoxic T-cell, helper T-cell, and B-cell epitopes from pathogen proteins using immunoinformatics tools, followed by screening for antigenicity, non-allergenicity, and non-toxicity. Selected epitopes are assembled with suitable linkers and adjuvants to enhance immune recognition and stability. Structural modeling, molecular docking, and dynamics simulation validate receptor binding and conformational integrity. This project aims to design a multi-epitope vaccine capable of eliciting strong cellular and humoral responses, providing a promising foundation for next-generation vaccine development.