Machine Learning in Neuroplasticity

Machine learning is unlocking new frontiers in neuropsychiatric research. In collaboration with Delix Therapeutics, Workflow Informatics applied machine learning to model neuroplasticity—predicting how small molecules influence neurite growth, a hallmark of compounds with antidepressant potential. By integrating curated chemical libraries, phenotypic screening, and XGBoost models trained on high-content image data, the project achieved a 65% hit rate for novel neuroplastogens. The result: powerful non-hallucinogenic candidates that outperform classic psychedelics in key assays. This white paper highlights how strategic use of ML and cheminformatics can drive discovery of next-generation therapeutics for depression and PTSD.