Dr Raman Sharma

Senior Post Doctoral Research Assistant

Raman received his Ph.D. in Computational/Theoretical Chemistry from the University of Manchester in 2006. He then took up a postdoctoral positions at Manchester and InhibOx Ltd. conducting research into the structure and function metallo-proteins and addressing the inadequacies of standard computer-aided drug design (CADD) approaches through the application of a range of quantum mechanical and molecular simulation methodologies aimed at modelling non-covalent and covalent interactions drug-protein interactions. 

In 2009 he joined LSTM as a senior postdoctoral researcher, where his primary research focus was the design of novel antimalarials through the application and development of structure and ligand-based CADD approaches which incorporated molecular docking, cheminformatics and machine learning techniques. In 2012, he took a position at Unilever PLC within the Maths and Informatics group, designing and deploying mathematical modelling and informatics solutions for a number of biologically focused Home and Personal Care projects. During this time he widened his modelling interests, successfully applying techniques such as PK-PD modelling, response surface modelling and experimental design. In 2013, he re-joined LSTM as PK/PD modeller working on the A-WOL-II drug development project, using PK/PD modelling to optimize dosing strategies for existing and novel anti-microbial therapies for Elephantiasis (Filariasis) and River Blindness (Onchocerciasis). 

He is currently project lead on a Wellcome Trust funded Institutional Strategic Seeding Fund project entitled “Early Prediction of the PK of Novel Endoperoxide Antimalarial Chemotherapies: A Cheminformatics Model to Predict Effective Drugs for Multidrug Resistant Plasmodium falciparum”. This project aims to predict preclinical pharmacokinetic parameters from the chemical structure and physicochemical properties of a library of endoperoxides through the application of QSAR/QSPR techniques. This technology platform will enable more accelerated and focused design of endoperoxides that match the optimal target product profile of a once-daily short antimalarial treatment conducive to high patient compliance and high systemic drug exposures which are required to overcome or mitigate against resistance: