Pablo Gainza
Director at Monte Rosa Therapeutics
Lausanne, Vaud, Switzerland
Summary
👤
Senior🎓
Top SchoolPablo Gainza is a director-level researcher and computational protein designer with 11 years of experience at the intersection of computer science and synthetic biology, currently leading programs at Monte Rosa Therapeutics in Lausanne. He develops geometric deep learning and computer-vision–based methods—ranging from heat kernel signatures and shape retrieval to modern deep learning—to design de novo protein binders and small-molecule–controlled protein switches for cell therapies. His work bridges rigorous algorithmic advances from a Duke PhD (including contributions to OSPREY) with wet-lab validation, accelerating translation from in silico models to functional molecules. An active contributor to the MaSIF project, he has deep experience processing molecular surfaces and implementing Python/BioPython pipelines for geometric deep learning. Colleagues know him for combining theoretical guarantees and scalable software engineering to tackle high-impact problems in therapeutics design.
10 years of coding experience
16 years of employment as a software developer
Doctor of Philosophy (PhD), Computer Science, Doctor of Philosophy (PhD), Computer Science at Duke UniversityBachelor's degree, Computer Science, Bachelor's degree, Computer Science at Universidad de Costa RicaMaster's degree, Computer Science, Master's degree, Computer Science at University of Costa Rica
Spanish, English, French
Github Skills (7)
geometric-deep-learning10
biopython10
python10
data-structure9
algorithm9
data-structures9
algorithms9
Programming languages (3)
Github contributions (5)
LPDI-EPFL/masif
Mar 2019 - Mar 2021
MaSIF- Molecular surface interaction fingerprints. Geometric deep learning to decipher patterns in molecular surfaces.
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:2 releases, 467 commits, 3 PRs in 2 years
Contributions summary:Pablo primarily contributed to the core functionality of the project by implementing methods for processing protein structures and computing coordinates. These changes involved extensive coding in Python with the use of the BioPython library. The contributions also extended to processing and manipulating surface data for geometric deep learning.
interactionproteinsgeometric-deep-learningdeep-learningstructural-biology
LPDI-EPFL/masif_seed
Feb 2022 - Jan 2023
Masif seed paper repository
Contributions:66 commits, 8 pushes in 10 months
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