D
Danishuddin
Department of Biotechnology, Yeungnam University, Department of Bio & Medical Big Data, Gyeongsang National University (GNU), Aligarh Muslim University, Jamia Millia Islamia
Research areas: Pharmacology, Toxicology and Pharmaceutics · Drug Discovery · Computational Drug Discovery Methods · Antibiotic Resistance in Bacteria · Synthesis and Characterization of Heterocyclic Compounds
0000-0001-9185-6709
Employment
-
Department of Biotechnology, Yeungnam University Research Professor2023 - Present
-
Department of Bio & Medical Big Data, Gyeongsang National University (GNU) Post Doctoral Researcher2021 - 2023
-
Jawaharlal Nehru University National Post Doc Fellow2018 - 2020
Education
-
Aligarh Muslim University Ph.D (Biotechnology), Interdisciplinary Biotechnology Unit2013 - 2016
-
Jamia Millia Islamia M.Sc (Bioinformatics), Department of Computer Science2005 - 2007
Projects & Funding
Projects & funding information is unavailable.
Publications (17)
- E3 ubiquitin ligases and their therapeutic potential in disease Management Save
- Machine Learning-Driven Consensus Modeling for Activity Ranking and Chemical Landscape Analysis of HIV-1 Inhibitors Save
- Unveiling the Mechanisms Underlying the Immunotherapeutic Potential of Gene–miRNA and Drugs in Head and Neck Cancer Save
- Exploring chemical space, scaffold diversity, and activity landscape of spleen tyrosine kinase active inhibitors Save
- Revolutionizing Drug Targeting Strategies: Integrating Artificial Intelligence and Structure-Based Methods in PROTAC Development Save
- From cancer big data to treatment: Artificial intelligence in cancer research Save
- Identification of Activated Cdc42-Associated Kinase Inhibitors as Potential Anticancer Agents Using Pharmacoinformatic Approaches. Save
- An attention mechanism-based LSTM network for cancer kinase activity prediction. Save
- A decade of machine learning-based predictive models for human pharmacokinetics: Advances and challenges. Save
- Development of Machine Learning Models for Accurately Predicting and Ranking the Activity of Lead Molecules to Inhibit PRC2 Dependent Cancer Save
- New Framework for the Discovery of PRC2 Inhibitors: Epigenetic Drugs. Save
- Development and rigorous validation of antimalarial predictive models using machine learning approaches. Save
- Polycomb repressive complex 2 inhibitors: emerging epigenetic modulators. Save
- Computer Aided Drug Design and its Application to the Development of Potential Drugs for Neurodegenerative Disorders. Save
- Descriptors and their selection methods in QSAR analysis: paradigm for drug design. Save
- Structure based virtual screening to discover putative drug candidates: necessary considerations and successful case studies. Save
- AMDD: antimicrobial drug database. Save