Bioinformatics y AI scientist
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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.
Job Title: Computational Chemist / AI Scientist
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.
Job Title: Bioinformatics and Systems Biology Intern
Field: Bioinformatics and Systems Biology
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
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
