Machine Learning for Natural-Product Discovery

Abstract

Antimicrobial drug-resistant pathogens pose a serious global threat, with millions projected to die each year by 2050. Natural products, namely secondary metabolites, play a crucial role in developing antimicrobial drugs. There is an urgent need for new antimicrobial drugs, which can be discovered by cultivating fungi under various conditions to stimulate the production of such secondary metabolites. This study examined mass-spectrometry data of Penicillium fungi grown under 13 different sub-conditions, using Principal Component Analysis (PCA) and sparse SPCA to find sub-condition differences. Sparse PCA was used to select mass-to-charge (m/z) bins that corresponded to potentially significant molecules, including secondary metabolites. Both PCA and sparse PCA effectively separated the growth sub-conditions, with sparse PCA providing some insights into m/z bins that differentiated growth sub-conditions. This study demonstrated that growth sub-conditions such as the type of light may have induced the fungi to produce interesting secondary metabolites, and that changes in nitrogen concentration significantly affected the m/z bin selections. Sparse PCA revealed notable trends in the selected m/z bins. Future research will focus on investigating other uses of machine learning with mass spectrometry for potential biological applications.