Artificial intelligence (AI) is transforming science and technology, from language translation to medical imaging. In chemistry and drug discovery, however, AI has not yet brought the same level of breakthrough. While deep learning (DL) tools like AlphaFold revolutionized protein structure prediction, their impact on small molecule discovery has been more incremental. A key reason is that, despite advances in algorithms, the basic “language” of molecules used by AI – such as SMILES strings and molecular graphs – has remained essentially unchanged for decades.
ReMINDER aims to address this gap by fundamentally rethinking how molecules are represented for AI applications. Instead of focusing solely on developing more complex algorithms, ReMINDER introduces entirely new molecular representation paradigms tailored for deep learning. These representations are designed to more richly encode chemical and biological information, allowing AI systems to better navigate molecular complexity and design novel therapeutics.
The project develops and benchmarks new representations that explicitly account for chemical properties, three-dimensional dynamics, and protein-ligand interaction features. These representations will be tested and validated across several drug discovery tasks, including de novo molecular design, structure-activity prediction, and structure-based screening. In doing so, ReMINDER seeks to build a new foundation for AI in chemistry, enabling models that are more efficient, generalizable, and able to tackle real-world challenges such as drug selectivity, polypharmacology, and molecular novelty.
