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Soham Choudhuri

Soham Choudhuri by Dr. Bhaswar Ghosh received  his doctorate in Computational Natural Sciences  (CNS). Here’s a  summary of his research work on Data-driven drug discovery and application in malaria:

Traditional drug discovery and development processes are time-consuming and costly, with only a fraction of compounds progressing to clinical testing and an even smaller percentage making it to market. To address these challenges, computer-aided drug discovery (CADD) methodologies have emerged as efficient tools for designing and evaluating potential drug candidates. By utilizing computational algorithms to model drug-receptor interactions, CADD significantly reduces costs and timeframes associated with lead identification and optimization while maintaining high quality. Integrating deep learning algorithms further enhances drug discovery pipelines by enabling the analysis of molecular structures, genetic data, and biochemical interactions to predict drug efficacy and toxicity, and optimize dosage regimens. Deep learning helps to design new small molecule drugs and peptide drugs. Nowadays, target-based drug design is giving promising results. We propose a novel computational pipeline that leverages single-cell transcriptomic data to identify crucial proteins as drug targets for malaria, a disease with increasing resistance to conventional treatments. Through mutual-information-based feature reduction and protein-protein interaction network analysis, key proteins vital for the survival of Plasmodium falciparum are identified, and potential drug molecules are computationally predicted using deep learning techniques. We can use this pipeline to select targets for any disease for developing drugs. Additionally, we explored peptides as promising therapeutic agents due to their targeted interactions with biological targets and reduced side effects compared to small-molecule drugs. We introduce HYDRA, a hybrid diffusion model for designing therapeutic peptides tailored to specific target receptors, exemplified by the design of peptides targeting Plasmodium falciparum Erythrocyte Membrane Protein 1 (PfEMP1) genes. HYDRA generates highly stable and diverse peptides based on a protein target. Gene expression is a multifaceted process crucial to understanding molecular biology and pharmacology. We worked on elucidating the intricate relationship between gene length and kinetic parameters, such as transcription rate (Si), association rate of transcription factor to bind with DNA (Kon), dissociation rate of transcription faction detached from DNA (Koff), Burst size (SKoff), which significantly influence the mean expression levels of genes.Using a two-state stochastic gene expression model implemented in Python, we analyzed single-cell transcriptomics data to predict kinetic parameters for each gene. We classified genes into short and long categories, revealing distinct patterns in the relationship between gene length and these parameters. Our results indicate that burst size plays a critical role in mean expression, highlighting its importance for identifying gene targets that require lower drug doses for therapeutic effects. This integrated computational framework holds promise for accelerating drug discovery efforts and combating drug-resistant diseases such as malaria.

January 2025