Faculty Publications

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Scholarly Publications by Integral Academia

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    Synthesis and Characterization of some Thiosemicarbazones and their Complexes
    (Book Rivers, 2026) Atiya Khan, Tahmeena Khan
    This chapter reports the synthesis and characterization of complexes of copper (II) and iron (II) with thiosemicarbazones of 2,4-dichlorothiosemicarbazon [Cu(C&H7C12N3S)2]SO4, [Fe(C8117Cl2N3S)2]SO4 and salicylaldehyde thiosemicarbazone [Cu(C8H9N3OS)2]SO4, [Fe(C8H9N3OS)2]SO4 have been described. The ligands and complexes were prepared through refluxing. The characterization was done by FT-IR, UV, and NMR spectroscopic techniques. The physicochemical parameters of the synthesized ligand and complexes were calculated using Molinspiration and SwissADME, Absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) properties were assessed using admetSAR. Physiochemical characteristics of synthesize ligands and their metal complexes were determined using Mol inspiration and SwissADME softwares. Properties like molecular weight topological polar surface area (IPSA), number of hydrogen bond donors and acceptors etc. were calculated to know the probable potential of molecules as lead candidates. TPSA was within the range i.e. below 102.73 A and the number of hydrogen bond donors and acceptors were also within the suggested range.
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    ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN DRUG DESIGN
    (M/S Academic Publishers & Distributors/ZENODO, 2025) Aleena, Pushpendra Soni, Ahsan Ahmed Khan, Mohammad Ahmad
    Artificial Intelligence (AI) and Machine Learning (ML) have transformed drug discovery, significantly improving efficiency, accuracy and speed in drug research and development. Compared to the older, time-consuming, and expensive traditional approach, AI-driven methodologies optimize molecular design, predict drug-target interactions and streamline clinical trial processesto the potential saving of both costs and timelines. AI-powered computational tools, such as virtual screening, de novo drug design, and structure-based drug development, have also helped enhance the selection of more accurate drug candidates to be brought to development, improving success in drug development. AI has also affected prediction in Drug-Drug Interactions (DDIs) and adverse drug reactions (ADRs), enhancing safety due to patient improvements from advanced techniques indata analysis like National Language Processing (NLP) and network-based modeling. Optimistic to this notion, clinical trials have become improved through AI: better patient selection, adaptive design and constant monitoring, maximizing efficiency while trimming costs. Currently, challenges encountered encompass data quality model interpretability along with ethical debates, yet research in pharmaceutical fields continues with momentum. Quantum Computing, automated laboratories and more such drugs repurposing by AI make it all work the faster way it is meant. This advancement of AI will continue to shape health care, hasten drug development, and enhance patients’ outcomes and therefore, is essential in modern medicine.