Leveraging ultra-large chemical spaces using accurate Quantum-Mechanics and Artificial Intelligence algorithms

Abstract

The accessible, synthesizable chemical space is expanding exponentially, now reaching trillions of virtual molecules—an unprecedented opportunity to identify novel, diverse hits. Yet, effectively exploring these ultra-large libraries remains a major computational and data challenge. In this talk, we show how Pharmacelera combines Quantum Mechanics and Artificial Intelligence to efficiently mine this space, uncovering novel scaffolds often missed by traditional approaches, with applications across Hit Identification, Hit-to-Lead, and Lead Optimization.

Bio

Enric Gibert is CEO and co-founder of Pharmacelera, a deep tech company that applies accurate Quantum Mechanics (QM) algorithms, Machine Learning (ML) and High-Performance Computing (HPC) for rational drug design. Pharmacelera is pushing the limits of computational chemistry to identify new therapeutic solutions for unmet medical needs by uncovering molecules that are totally missed using traditional methodologies.

Enric Gibert holds a PhD degree in computer engineering from Universitat Politècnica de Catalunya. He worked at the Intel Barcelona Research Center (IBRC) for 10 years on the design of new High-Performance Computing (HPC) architectures, being technical leader and manager of international teams from the United States, Israel, Spain and Germany. After that, he enrolled a business degree from IESE Business School and founded Pharmacelera. Enric Gibert is co-author in 25 publications and 20 filed patents in the fields of High-Performance Computing, algorithms, and computational chemistry.
 

Hosted by Pharmacology Applied to Drug Discovery Group, CiMUS

Certificates of attendance will be provided upon request at cimus.xestion [at] usc.es (cimus[dot]xestion[at]usc[dot]es). Please do not forget to enter your name and surname in the printout given during the lecture.