Priyanka Sharma BIoinformatics, Computational Biology, Docking, MD
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I am a Computational Biology researcher with a strong focus on AI-driven approaches in drug discovery, molecular interactions, and disease prediction. My work integrates bioinformatics, machine learning, and structural biology to solve real-world biomedical problems, particularly in cancer research. I specialize in RNA–ligand, protein–ligand, and aptamer–protein interaction analysis, with hands-on experience in molecular docking, molecular dynamics simulations, and binding affinity prediction. I have developed AI-based models using techniques such as Random Forest, XGBoost, and deep learning to predict molecular interactions and optimize therapeutic candidates. My teaching approach is concept-driven and application-oriented. I believe in simplifying complex topics like bioinformatics, machine learning, and structural biology through step-by-step explanations, real datasets, and practical examples. I guide students not only in understanding theory but also in implementing projects using tools like Python, AMBER, RDKit, and deep learning frameworks. I have experience mentoring students in research projects, helping them design workflows, analyze biological data, and build AI-based models. My goal is to help students gain both conceptual clarity and hands-on skills so they can confidently work on academic, research, or industry-level problems.

If you are interested in learning bioinformatics, computational biology, or applying AI in life sciences, I can help you build strong fundamentals and practical expertise.

Subjects

  • Science & Technology Beginner-Expert

  • Computational Bioinformatics Beginner-Expert

  • Advanced Bioinformatics Beginner-Expert

  • Biology (10th)

Experience

No experience mentioned.

Education

  • Bioinformatics (Jan, 2024–now) from Bennett University, Greater Noida

Fee details

    ₹10,000–20,000/month (US$105.25–210.50/month)

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