Lluís Giménez Gabarró

I consider myself a highly proactive individual, always looking for new challenges and opportunities to take my career to the next level in Computer-Aided Drug Design (CADD), structural bioinformatics, and Machine Learning. I could not be more convinced that those are the domains where I can exploit my full potential.

Company: Nostrum BioDiscovery

Job Title: Computational Chemist / AI Scientist 

Field: Drug Discovery

Role Description:

In my most recent role, I served as a Computational Chemist / AI Scientist in the field of drug discovery. My primary responsibility was to leverage molecular modeling and artificial intelligence techniques to advance the discovery of novel therapeutics, specifically focusing on developing superior degrader molecular glues (MGs). I played a pivotal role in training, fine-tuning, and validating transformer graph networks and generative models. As a result, I was able to screen 5 billion compounds within 2 hours and I was able to generate novel compounds with enhanced properties, which led to promising results that need to be experimentally validated. 

My experience encompasses a deep understanding and practical application of molecular modeling techniques, AI algorithms (including surrogate and generative models), virtual screening methodologies, and computational chemistry principles. I have a proven track record of innovation in generating novel compounds with therapeutic potential and improving the efficiency and accuracy of drug discovery processes.

Company: Made of Genes

Job Title: Bioinformatics and Systems Biology Intern

Field: Bioinformatics and Systems Biology

Role Description:

Specialized in the creation of an automatized algorithm that creates dynamic petitioners with DocuSign and stores them in a docker. Understanding of the MoG workflow, detection of bugs, and proposal of alternatives to increase the efficiency of the algorithms used.

Company: JOSEP CARRERAS LEUKEMIA RESEARCH INSTITUTE

Job Title: Bioinformatics analysis services & Software development

Field: Cancer Epigenetics

Role Description:

During my internship at the Josep Carreras Leukaemia Research Institute (IJC) with the Bioinformatics Unit, I worked under the guidance of Angelika Merkel and in collaboration with Manel Esteller’s Group for Cancer Epigenetics. My key responsibilities involved COVID-19 epigenetics analysis.

I analyzed DNA methylation differences between healthy individuals and COVID-19 patients using the Illumina Infinium MethylationEPIC microarray. To do this, I developed an R script based on the "minfi" analysis workflow to perform differential methylation analysis, encompassing quality control, filtering, normalization, data exploration, and statistical testing. I then compared my analysis results with those produced by "VisualMethyl," an R Shiny application developed by Irene Fernandez in Manel Esteller's group, verifying consistency between my script and the application.

I also served as a Beta-Tester for "VisualMethyl," providing a report on potential improvements and error resolutions to enhance the application.

I finally contributed to a project aimed at integrating "VisualMethyl" with "MethylationDB," a MongoDB-based database and JavaScript web application for storing and visualizing methylation data. This provided me with the fundamentals of MongoDB, JavaScript, HTML, and CSS to support the integration efforts, creating a tutorial to document my learning process.

Bioinformatics Bachelor's Degree | Universitat Pompeu Fabra (ESCI-UPF)                                06/2024

• Average Grade: 9.2 out of 10 (top 1%).
• Awarded with the first prize for the Best Bachelor's Thesis 2023/2024.
• Academic Achievements and Honors: (1) Introduction to Bioinformatics, (2) Molecular Biology, (3) Biophysics, (4) Structural Bioinformatics, (5) Databases, (6) Omics, (7) High-Performance Computing, (8) Systems and Network Biology, (9) Statistical Models and Stochastic Processes.

Biotechnology bachelor's degree | Universitat Autònoma de Barcelona (UAB)                          06/2023 

• Average Grade: 8.1 out of 10 (top 15%).
• Academic Achievements and Honors: (1) Numerical Methods and Informatic’s Applications, (2) Genetic Engineering of Microorganisms, (3) Genetic Improvement in Animals.