Currently I am working on 'AIDrugApp' projects in collaboration with Dr. Vikas Karade (https://www.linkedin.com/in/vikaskarade/) as a Mentor from Sepetember 2020 and Dr. Damini Kolhe (https://www.linkedin.com/in/dr-damini-kolhe-27873a1a7/) as a Team Member from Sepetember 2021. This is a self conceived multi - project work which was first started for drug discovery hackathons. My PhD work is focussed on drug designing and is highly inter-disciplinary encompassing the fields of cheminformatics, computational biology and metabolomics described below.
Secondary metabolites produced by plants are valuable for their essential roles in food, medicine, and agrochemicals. The challenges identified in this study will serve as a useful reference for future intensive research in drug and pesticide discovery.
This study aimed to report a computational protocol as well as a tool for generating novel potential drug candidates from the bioactive molecules of Indian medicinal and aromatic plants through the chemoinformatics approach.
This study intended to identify, analyze and design a virtual library of prioritized novel and promising drug-like molecules based on analysis of secondary metabolites of soybean using chemoinformatics and untargeted mass spectrometry (UHPLC-MS/MS) approaches.
In this study, we developed a computational protocol to identify allelochemicals with pesticide-like properties through the chemoinformatics approach. We demonstrated their pesticidal activities by screening them with pesticide-likeness and QSAR modeling.
AIDrugApp (https://divyakarade-aidrugapp-covid-app-7v34bs.streamlit.app/) is a National award-winning ongoing self-conceived project to develop an AI-based virtual screening web-application platform for drug discovery.
In this work, I developed a Deep learning classification-based model for screening pharmaceutical compounds with hERG inhibitory activity (cardiotoxicity) and using the model to screen CAS antiviral database to identify compounds with cardiotoxicity potential. Data is taken from "Drug Discovery Hackathon 2020: PS ID: DDT2-13" (https://innovateindia.mygov.in/ddh2020/problem-statements/)
AWS deepracing (https://aws.amazon.com/deepracer/) is a virtual autonomous racing league, driven by Machine Learning (ML) where one has to build ML models through a cloud based 3D racing simulator, fully autonomous 1/18th scale race car driven by reinforcement learning, and hit the tracks virtually. In GitHub repository I kept my best reinforcement learning codes for AWS deepracing which helped me to reach national level in community races.
This study deals with the designing of novel antidiabetic drug-like and lead-like molecules from fenugreek organic metabolites using Chem-Bioinformatics tools and techniques. This work is presented at international conference "Accelerating Biology 2019: Towards Thinking Machines" organised by CDAC, India.
Completed Ph.D. from CSIR - National Chemical Laboratory (India's most prestigious research labs and an academic center of AcSIR i.e. Academy of Scientific and Innovative Research) on December 2021. In addition, I worked as a Senior Research Fellow at the CSIR-National Chemical Laboratory. My research is highly inter-disciplinary and collaborative including biotechnology, chemistry & computational science fields. The thesis is accessible at the CSIR-National Chemical Laboratory Digital Repository. All the supplementary data is made available at Zenodo and GitHub as a reviewed Ph.D. thesis work adhering to the FAIR data standards. My future research interests are developing machine learning methods and tools for biology and medicine. More details can be found on my Resume.