Gianni De Fabritiis

Group website

Research Outline

Our research interests are rooted in the application of computation to solve real-world problems, based on the view that computation and intelligence are very much the same thing. Specifically, we develop new methods, programs and algorithms that we apply to areas such as AI, drug design, and protein folding. The group and its spin-off company Acellera has collaborated with major industries worldwide like Sony, Nvidia, HTC mobile, UCB, Pfizer, Biogen, Novartis, etc.

 

Research Lines

Main research lines:

Biomedicine: We use computations, as physics-based simulations and machine learning to provide novel, innovative approaches in biomedicine

Machine Intelligence: We develop machine learning models with the aim to attain in specific environments intelligent, useful behavior using reinforcement learning and deep learning

 

Main Projects:

 

  • EU H2020 CompBiomed: A Centre of Excellence in Computational Biomedicine (2); (GA 675451 H2020-EINFRA-2015-1) Period 1/10/2016 to 30/09/2019. Funding : 191.750 €.

  • Industrial Doctorate Alejandro Varela (AGAUR 2017 DI 067) in collaboration with Acellera, Period 1/2/2018 to 31/1/2021. Funding: 33.960 €.

  • EU H2020 CompBiomed2: A Centre of Excellence in Computational Biomedicine (2); (GA 823712)Period 1/10/2019 to 30/09/2023. Funding : 357.000 €.

  • H2020-MSCA-ITN  AIDD: Advanced machine learning for Innovative Drug Discovery; (GA 956832); Period: 1/12/2020 to 30/11/2022; Funding: 125.452 €.

  • MINECO; Aplicación de métodos de aprendizaje automático y de aumento de datos en el estudios de la conformación proteica y el reconocimiento molecular para el desarrollo de fármacos. BIO2017-82628-P; Period 01/01/2018 to 31/12/2020. Funding 260.150 €.        

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Team during 2019-20

  • PhD students: Boris Sattarov, Dominik Lemm, Miha Skalic, Gabriele Libardi, José Jiménez, Adrià Pérez, Alejandro Varela.

  • Postdocs: Davide Sabbadin, Stephan Doerr, Maciej Majewski.

  • Technicians: Albert Bou, Sebastian Dittert

     
     

Selected publications 

  • Rodríguez-Espigares I, Torrens-Fontanals M, Tiemann JKS et al (including Selent J, Sanz F, de Fabritiis G). GPCRmd uncovers the dynamics of the 3D-GPCRome. Nat Methods, 2020; 17(8): 777-787.
  • Landin EJ, Lovera S, de Fabritiis G, Kelm S, Mercier J, MacMillan D, Sessions R, Taylor R, Sands ZA, Joedicke L, Crump MP. The Aminotriazole Antagonist Cmpd-1 Stabilises a Novel Inactive State of the Adenosine 2A Receptor. Angew Chem Int Ed Engl, 2019; 58(28): 9399- 9403.
  • Jiménez-Luna J, Pérez-Benito L, Martínez-Rosell G, Sciabola S, Torella R, Tresadern G, De Fabritiis G. DeltaDelta neural networks for lead optimization of small molecule potency. Chem Sci, 2019; 10 (47): 10911-10918.
  • Noé F, De Fabritiis G, Clementi C. Machine learning for protein folding and dynamics. Curr Opin Struct Biol, 2020; 60: 77-84.
  • Pérez A, Herrera-Nieto P, Doerr S, De Fabritiis G. AdaptiveBandit: A multi-armed bandit framework for adaptive sampling in molecular simulations. J Chem Theory Comput, 2020; 16(7): 4685-4693.