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ReMINDER

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Revolutionizing AI in drug discovery via innovative molecular representation par...

Revolutionizing AI in drug discovery via innovative molecular representation paradigms Artificial intelligence (AI) in the form of deep learning is driving unprecedented progress in numerous fields, e.g., for protein structure prediction and organic reaction planning. In drug discovery and chemical biology, such pro... ver más

31/12/2027

TU/e

1M€

Presupuesto del proyecto: 1M€

Líder del proyecto

TECHNISCHE UNIVERSITEIT EINDHOVEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-12-08

ERC-2022-STG: ERC STARTING GRANTS Scope:Objectives

I+D

Cerrada hace 2 años

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1 Participantes

TU/e

1.49M€ | Lider

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Duración del proyecto: 60 meses Fecha Inicio: 2022-12-08
Fecha Fin: 2027-12-31

Líder del proyecto

TECHNISCHE UNIVERSITEIT EINDHOVEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 1M€

Descripción del proyecto Artificial intelligence (AI) in the form of deep learning is driving unprecedented progress in numerous fields, e.g., for protein structure prediction and organic reaction planning. In drug discovery and chemical biology, such progress is an evolution rather than a revolution: several tasks still await to be solved by AI, e.g., accurate structure-activity and activity-cliff prediction, and design of structurally innovative chemical matter. Increasingly complex deep learning approaches are leading to progressively smaller gains in model capabilities, calling for a revolution in AI for drug discovery. The springboard for this project is a striking observation: while novel deep learning algorithms are in continuous development, the input ‘raw’ molecular representations they rely on (e.g., SMILES strings and molecular graphs) have not considerably changed in the last four decades – limiting the amount and quality of chemical information learnable by AI. The potential of capturing more sophisticated chemical information better into a new ‘molecular language’ is still untapped and bears promise to revolutionize molecular AI. ReMINDER will break with traditional approaches and shift the object of study from increasingly complex algorithms to novel molecular representation paradigms for AI. ReMINDER will be an agent of change in the molecular AI landscape, by developing a new representation framework at the interface between method development and experimental validation. ReMINDER will disrupt the potential of AI to (a) navigate complex structure-activity landscapes, (b) design innovative bioactive molecules from scratch, (c) leverage binding pocket information for molecule discovery. By transforming the chemical information captured for AI, we open opportunities to develop more efficient models and solve open scientific challenges. ReMINDER will create the basis for exciting new technology in the field of deep learning for drug discovery, and chemistry at large.

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