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个人简介
I have a strong commitment to working in bioinformatics and computer-aided drug design. I have completed my M.S. (Pharm.) Pharmacoinformatics (June 2017) under the supervision of Dr. Prabha Garg (Professor & Head, Pharmacoinformatics) from National Institute of Pharmaceutical Education and Research (NIPER) SAS Nagar Mohali with one year project entitled “Prediction of Antimicrobial Peptides Using Machine Learning Approach”. My research project aimed to predict the antimicrobial peptides which find application as antibiotics, anticancer, and anti fungals.
Recently I completed my doctoral degree in Pharmacoinformatics from the national institute of pharmacy (National Institute of Pharmaceutical Education and Research, Kolkata, India) and my project was based on computational molecular modelling and artificial intelligence.
Ph.D. thesis title: “Computational modelling for the design and optimization of novel small molecules as potential inhibitors of the mycobacterial cytochrome bc1 complex”. I have designed 20 molecules computationally for tuberculosis by using molecular modelling methods. I also developed machine learning classification models for molecules that act as anti-tubercular and published that work in a good journal. I have published 9 papers in good journals during my doctoral degree.
In computer proficiency, I have a very good experience with MS-word, MS-excel, Rstudio, Schrondiger suit (maestro and canvas), and Molecular dynamic simulations (Desmond). Programming languages like Python, and R-language and Scientific software packages like Jupyter, ChemDraw, and Endnote.
I can be reached at any time via email: wanihayat10@gmail.com or phone at 6005756569
Research Interests:
Development of machine learning models in the pharmaceutical domain.
Applications of artificial intelligence in drug discovery and healthcare.
Molecular modeling to study molecular interactions and drug behavior.
Pharmacophore mapping for drug design and target identification.
研究兴趣
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Trends in biochemical sciencesno. 3 (2024): 195-198
Social Science Research Network (2020)
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