SilicoLife leading DeepBio focused on the application of deep learning to industrial biotechnology

SilicoLife, University of Minho and NOVA University of Lisbon were awarded a national grant for the development of novel approaches for the computational design of new molecules and biotransformations with enhanced capabilities, boosted by the use of methods and technologies from machine learning and deep learning.

The project DeepBio will focus on the development of new AI tools for the prediction of biological activities of relevant molecules, the generation of new molecular structures, reactions and metabolic pathways.

Technical sheet / Ficha técnica (in Portuguese)
Projeto I&DT Empresas em Copromoção

Designação do Projeto / Project title: DeepBio – Abordagens de aprendizagem máquina e deep learning para aplicações de biotecnologia industrial
Código do Projeto / Project code: NORTE-01-0247-FEDER-039831

Objetivo Principal / Main aims: 
Reforço da investigação, do desenvolvimento tecnológico e da inovação
Promotion of research, technological development and innovation

Região de Intervenção / Region: Norte, Lisboa

Organismos Financiadores: Lisboa 2020; NORTE2020; Portugal 2020; FEDER – Fundo Europeu de Desenvolvimento Regional

Entidade Beneficiária / Beneficiary: SilicoLife Lda., Universidade do Minho, Universidade Nova de Lisboa

Data de início / Starting date: 01-07-2019

Data de conclusão / Conclusion date: 30-06-2022

Custo total elegível SilicoLife / Total eligible costs:  378.815,95 euros

Apoio financeiro da União Europeia SilicoLife / Financial support European Union SilicoLife: FEDER – 283.569,44 EUR

Apoio financeiro da União Europeia TOTAL / Financial support European Union TOTAL: FEDER – 681.648,00 EUR

Objetivos, atividades e resultados esperados / Objectives, activities and expected results:
Desenvolvimento de métodos computacionais de engenharia metabólica com base em Aprendizagem Máquina/ Deep learning para a previsão de atividades biológicas de moléculas e a geração de novas moléculas, reações e vias metabólicas para aplicações de biotecnologia industrial.
Development of computational methods for metabolic engineering based on Machine Learning / Deep Learning for the prediction of biological activities, new molecules, reactions and pathways for industrial biotechnology applications.




Press release Braga, July 4, 2019

SilicoLife, University of Minho and NOVA University of Lisbon were awarded a national grant for the development of novel approaches for the computational design of new molecules and biotransformations with enhanced capabilities, boosted by the use of methods and technologies from machine learning and deep learning.

This new 3-year project, beginning this July, contributes to expand SilicoLife’s leadership in the use of AI methodologies in the industrial biotechnology context, promoting the development of computational solutions for tasks such as the prediction of the biological activities of relevant molecules for metabolic processes and their interactions, the generation of new molecules, reactions and metabolic pathways with specific activities, and the definition of new bioprocesses through enzyme optimization.

“The application of deep learning methods will allow us to expand nature’s diversity combining the design of new-to-nature molecules with the creation of biological and sustainable routes to produce them” said Simão Soares, SilicoLife CEO. “This project is aligned with our efforts to transform industries leveraged on the combination of AI and biology”.

The project with a budget of six hundred thousand euro combines the industrial leadership of SilicoLife with the recognized research experience of the Center of Biological Engineering (CEB) from University of Minho the Instituto de Tecnologia Química e Biológica António Xavier (ITQB-NOVA) from NOVA University of Lisbon. DeepBio is co-financed by Portugal 2020 (Norte2020 and Lisboa2020), the partnership agreement between Portugal and the European Commission for the promotion of policy economic, social and territorial development in the country.

Project: NORTE-01-0247-FEDER-039831 / LISBOA-01-0247-FEDER-039831