Dr. Vibhu Jha

Biography

Vibhu Jha has studied B. Pharm. from Chhattisgarh Swami Vivekananda Technical University (CSVTU), Bhilai, India (2008 – 2012). Vibhu undertook M. S. Pharm. (Medicinal Chemistry) from the National Institute of Pharmaceutical Education and Research (NIPER), Raebareli, India (2013 – 2015). As a part of his masters dissertation, he spent one year at the CSIR – Central Drug Research Institute (CDRI), Lucknow, India, working on ‘In silico studies and synthesis of antidiabetic agents.’ Vibhu subsequently joined School of Pharmacy, ITM University, Gwalior, India as an Assistant Professor carrying out teaching and research activities at UG level (2015 – 2017). Vibhu obtained an international fully-funded PhD position at the Department of Pharmacy, University of Pisa, Italy and was awarded ‘Excellent honours’ (2017 – 2021). His PhD was focused on molecular modeling and virtual screening studies to identify new anticancer hit compounds, against druggable protein targets. In addition, Vibhu spent 6-months as a visiting PhD researcher at the Institute of Cancer Therapeutics (2019), gaining medicinal chemistry experience.

Vibhu joined School of Life Sciences, University of Dundee, UK, as a Researcher in Molecular Modeling, focussing on antitubercular and antimalarial drug discovery (2021 – 2022). Vibhu subsequently moved to the Department of Chemistry and Molecular Biology, University of Gothenburg, Sweden as a Postdoc (2022 – 2023), working on various anticancer drug discovery projects driven by the state-of-the-art computational modeling techniques, also including masters project supervision, and teaching. Vibhu joined Institute of Cancer Therapeutics as an Assistant Professor in Computational Drug Design. Vibhu utilizes conventional computational modeling tools such as docking, molecular dynamics, virtual screening etc along with the state-of-the-art Artificial Intelligence and Machine Learning tools to study protein – ligand, protein – protein , protein – DNA interactions, that further helps to develop small molecule inhibitors/modulators against various cancer types. Vibhu is an active collaborator with different research groups in Italy, Sweden, Germany and India. 

Research

My research involves utilizing a wide range of computational modeling techniques such as homology modeling, docking, molecular dynamics simulations, QM/MM simulations and binding free energy calculations to study protein – ligand and protein – protein interactions, their mechanism of activation/inhibition relevant to cancer. The research is further supported by structure-based and ligand-based drug design studies involving high-throughput virtual screening, pharmacophore and QSAR modeling to develop new hit and lead compounds against cancer drug targets as following:

(A) Targeting 1C metabolism pathway to develop selective inhibitors of methylenetetrahydrofolate dehydrogenases (MTHFD2), serine hydroxy methyltransferase 2 (SHMT2) and dihydrofolate folate reductases (DHFR) against breast and colorectal cancers.

(B) Developing dual inhibitors of mutated epidermal growth factor receptor tyrosine kinases EGFR (L858R/T790M)/ACK1 as a new therapeutic strategy to overcome drug resistance in lung cancers.   3

(C) Developing inhibitors that simultaneously inhibit human topoisomerases (hTopos) and histone deacetylases (HDACs), showing dual mode of inhibition against lung cancer.

Artificial intelligence and machine learning routines such as random forest (RF), support vector machine (SVM), artificial neural network (ANN) are furthermore employed to develop predictive and generative models against (1) ALDH1A1, ALDH1A2, ALDH1A3, ALDH3A1 and ALDH71 isoforms (2) EGFR (L858R/T790M) and ACK1 kinases, using huge chemical databases.

Research collaborator

• Medicinal Chemistry and Pharmacology (Department of Pharmaceutical Sciences and Natural Product, Central University of Punjab)
• Computational Drug Discovery, Artificial Intelligence and Machine Learning in Drug Discovery (Department of Chemistry and Molecular Biology, University of Gothenburg)
• Medicinal Chemistry, Computational Drug Discovery, Artificial Intelligence and Machine Learning in Drug Discovery (Department of Pharmacy, University of Pisa)

Professional activities

Publications