{"id":5140,"date":"2026-06-22T11:57:24","date_gmt":"2026-06-22T11:57:24","guid":{"rendered":"https:\/\/cognitionconferences.com\/traditionalmedicine\/?post_type=speaker&p=5140"},"modified":"2026-06-22T11:57:24","modified_gmt":"2026-06-22T11:57:24","slug":"sohini-chakraborty","status":"publish","type":"speaker","link":"https:\/\/cognitionconferences.com\/traditionalmedicine\/speaker\/sohini-chakraborty\/","title":{"rendered":"Sohini Chakraborty"},"content":{"rendered":"

Abstract\u00a0<\/strong><\/p>\n

Breast cancer is one of the most prevalent malignancies among women worldwide, with estrogen biosynthesis playing a pivotal role in tumor growth and progression. Although endocrine therapies have improved clinical outcomes, adverse effects and drug resistance remain major challenges, highlighting the need for novel therapeutic alternatives. Traditional medicinal plants represent a valuable source of bioactive compounds with potential anticancer properties. This study employed an integrated in-silico approach combining network pharmacology, molecular docking, and molecular dynamics simulations to identify key molecular targets involved in estrogen biosynthesis and evaluate phytocompounds as potential inhibitors. Network analysis of estrogen biosynthesis-associated genes revealed six hub mRNAs (AKR1C3, STS, CYP11A1, ESR1, ESR2, and AR) within a regulatory network comprising mRNAs, miRNAs, lncRNAs, and small molecules. Expression and survival analyses further supported their relevance in breast cancer pathogenesis. Selected phytocompounds were screened for drug-likeness and toxicity and subsequently subjected to molecular docking studies. Among the compounds evaluated, Resibufogenin and Bassianin exhibited stronger binding affinities toward AKR1C3 than the reference drug Tamoxifen. The stability of the protein\u2013ligand complexes was further assessed through molecular dynamics simulations using RMSD, RMSF, SASA, radius of gyration, principal component analysis, and free energy landscape analyses. Both phytocompounds demonstrated stable interactions and favorable binding characteristics throughout the simulation period. These findings highlight the therapeutic potential of traditional medicinal phytocompounds targeting estrogen biosynthesis in breast cancer and identify Resibufogenin and Bassianin as promising candidates for further experimental validation.<\/p>\n","protected":false},"featured_media":5139,"template":"","meta":{"_acf_changed":false},"schedule":[],"speaker-category":[],"class_list":["post-5140","speaker","type-speaker","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\nSohini Chakraborty | REVA University | India<\/title>\n<meta name=\"description\" content=\"Dr. Sohini Chakraborty is a bioinformatician and computational biologist specializing in cancer systems biology, network pharmacology, RNA therapeutics, and AI-driven biomedical research. Her work integrates multi-omics analysis, machine learning, molecular docking, molecular dynamics simulations, and computational drug discovery to identify novel biomarkers and therapeutic targets. She has expertise in bioinformatics tools, biological databases, Cytoscape, and GROMACS, with research contributions in precision medicine and translational bioinformatics. Dr. Chakraborty has published multiple articles in reputed journals and actively conducts workshops and academic training programs in computational biology, bioinformatics, and AI applications in life sciences.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/cognitionconferences.com\/traditionalmedicine\/speaker\/sohini-chakraborty\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Sohini Chakraborty | REVA University | India\" \/>\n<meta property=\"og:description\" content=\"Dr. Sohini Chakraborty is a bioinformatician and computational biologist specializing in cancer systems biology, network pharmacology, RNA therapeutics, and AI-driven biomedical research. 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