Our research focuses on developing AI, machine learning, and deep learning tools for drug discovery related to human health and host-pathogen interactions. Additionally, our lab is leveraging machine learning algorithms to analyze image-based datasets.
Major Research Projects
Machine Intelligence Approaches For Drug Discovery
Machine learning in Virtual screening is always challenging and still evolving. Our lab has developed A-HIOT tool which is making a reliable approach for bridging the long-standing gap between ligand-based and structure-based VS in finding the optimized hits for the desired receptor.
We are now working on utilizing and improving A-HIOT tool and validating protocol. Any ideas on improvements/suggestions are welcome to join this project.
Key Publications:
- Machine intelligence-driven framework for optimized hit selection in virtual screening Journal of Cheminformatics 14, 48 (2022) Article (IF=8.48)
- Machine intelligence-guided selection of optimized inhibitor for human immunodeficiency virus (HIV) from natural products Computers in Biology and Medicine, 153:106525 (2023) Article (IF=7.7)
- Advances in machine intelligence-driven virtual screening approaches for big-data Medicinal Research Reviews, 44:939-974 (2023) Article (IF=13.33)
Elucidating Host-Pathogen Rat Race through Deep Learning
Host-pathogen interactions are always challenging and rat race continuation still thrills. We have developed DLNet algorithm which is the first ever implementation of integrated deep learning and network biology approach to understand the adaptation of plant immune genes in response to multiple pathogens using genomics data.
Keeping with rat race, we are working on developing effector software prediction tool.
Key Publications:
- Deep learning uncovers distinct behaviour of rice network to pathogens response iScience, Cell Press (2022) Article (IF=6.107)
- Deep learning based protocol to construct an immune-related gene network of host-pathogen interactions in plants STAR Protocols, Cell Press, 4:101934 (2023) Article
- Effector protein structures: a tale of evolutionary relationship Trends in Plant Science, 28:746-748 (2023) Article (IF=22.01)
Big Data Analysis of Human Health
1. Oxidative Stress in Human Cancer
- DNA damage response proteins synergistically affect the cancer prognosis and resistance Free Radical Biology and Medicine (2021) Article (IF=8.1)
2. Computational Analysis of Neurodegenerative Disorders
- Leveraging Big Data & Empirical Analysis for Post-stroke Cognitive Impairment Upcoming
- Leveraging MRI & AI for cost-effective Alzheimer's severity assessment Upcoming
Imaging Analysis of Plant Data
1. AI-powered Stomata Analysis of Amaranthus diverse genotypes under stress conditions
Active Project2. AI-powered grading of Tea Leaves
Active ProjectNote: These projects involve developing machine learning models for automated analysis of plant morphological features and quality assessment.
Plant Imaging Analysis Research