Revolutionizing AI in drug discovery via innovative molecular representation paradigms

Introduction

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.

Current Challenges

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.

Potential for Improvement

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.

Objectives of ReMINDER

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:

1. Navigate complex structure-activity landscapes,
2. Design innovative bioactive molecules from scratch,
3. Leverage binding pocket information for molecule discovery.

Conclusion

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.