Open Positions
PhD Fellowship on "In silico methods for the early drug development and formulation“
One PhD fellowship is available at the Department of Chemistry, Sapienza University of Rome funded by the PON initiative.
The successful candidate will work on "In silico methods for the early drug development and formulation“. The project might involve a collaboration with a pharmaceutical company.
A master degree in physics, chemistry, biological sciences or related disciplines is required. Experience with molecular dynamics and a solid background of statistical mechanics is a plus. The deadline is October, 27 2021.
Details of the application procedure can be found here:
1) https://phd.uniroma1.it/web/concorso37pon.aspx?i=3536
2) https://www.uniroma1.it/it/pagina/dottorati-di-ricerca#pon-ricerca-e-innovazione-2014-2020---avviso-aggiuntivo-di-ammissione-a-posti-di-dottorato-su-tematiche-green-e-dell-innovazione-con-borsa-per-l-a-a-2021-22---37-ciclo
Short Description of the project
This PhD project is focused on the development of methods aimed at the computer-aided drug development and formulation. The project is articulated in two parts. In the first part, the student will focus on applying molecular dynamics methods to investigate the interactions between drug-excipients, evaluating their impact on the aggregation and post-translational modifications (PTMs) of the drug. The approach will allow to generate drug-excipient descriptor databases to be used in the context of AI-methods for drug formulation prediction. That brings the student to the second part of the PhD project, which is about the development of AI-based methods for the prediction of structural PTMs. In this part of the project, the student will make extensive use of advanced theoretical-computational approaches to rationalize and understand the structural and chemical drivers causing the drug modifications. As a result, the combination of a complex set of molecular descriptors with prior knowledge data will be instrumental to generate training sets aimed for the application of AI-based methods. Final goal of this project is to develop a tool for predicting the criticality associated with PTMs for any newly designed biotherapeutic drug. Notably, along the three years of the PhD path, the student will acquire familiarity with state-of-the-art methods in computational structural biology and chemistry as well as with the most recent techniques of data science and machine learning.
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For further information about the project, send an email to
Prof. Marco D'Abramo
Dept. of Chemistry, Sapienza University of Rome
marco.dabramo@uniroma1.it
We are always willing to consider job applications at pre-doc and post-doc levels.
If you are interested in joining the group, please contact me by email
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