A Self-feedback Knowledge Elicitation Approach for Chemical Reaction Predictions

arXiv:2404.09606v1 Announce Type: new
Abstract: The task of chemical reaction predictions (CRPs) plays a pivotal role in advancing drug discovery and material science. However, its effectiveness is constrained by the vast and uncertain chemical reaction space and challenges in capturing reaction selectivity, particularly due to existing methods' limitations in exploiting the data's inherent knowledge. To address these challenges, we introduce a data-curated self-feedback knowledge elicitation approach. This method starts from iterative optimization of molecular representations and facilitates the extraction of …

abstract arxiv challenges cs.ai cs.lg discovery drug discovery feedback however knowledge limitations material pivotal predictions q-bio.qm role science space type uncertain vast