Synthetic Data from Diffusion Models Improve Drug Discovery Prediction

arXiv:2405.03799v1 Announce Type: new
Abstract: Artificial intelligence (AI) is increasingly used in every stage of drug development. Continuing breakthroughs in AI-based methods for drug discovery require the creation, improvement, and refinement of drug discovery data. We posit a new data challenge that slows the advancement of drug discovery AI: datasets are often collected independently from each other, often with little overlap, creating data sparsity. Data sparsity makes data curation difficult for researchers looking to answer key research questions requiring values …

abstract advancement artificial artificial intelligence arxiv challenge cs.ai cs.lg data datasets development diffusion diffusion models discovery drug development drug discovery every improvement intelligence posit prediction q-bio.qm stage synthetic synthetic data type