“It’s like a paradigm shift approach… to drive discovery”: a new machine-learning model predicts how molecules will influence gene expression and has been used to pick out promising drug candidates for two tough-to-treat diseases. In a multi-institute collaboration led by Michigan State University (MI, USA), scientists have created a machine-learning-based drug discovery platform, guided by transcriptomic features, which can be used to screen large compound libraries and optimize lead molecules. Potential therapeutics identified in the study were subject to real-world testing in human cell lines and animal models, ultimately yielding promising new drug candidates for two hard-to-treat diseases: hepatocellular carcinoma (HCC) – the third leading cause of cancer-related death worldwide – and the rare chronic lung disease idiopathic pulmonary fibrosis (IPF). Identifying drugs that reverse the expression of disease-associated transcriptomic features has been widely explored for identifying drug repurposing candidates, but its potential for de novo drug discovery remains underexplored. To implement such an approach for screening ultra-large compound libraries, gene expression profiles of the compounds are required. These can be used to train machine learning models so that they can infer gene expression based solely on chemical structures. Despite recent successes demonstrating the potential of using this method in preclinical drug discovery, so far, studies have only included commonly studied compounds, and they have not yet investigated novel compounds or performed lead optimization, an essential step in early drug discovery. Integrating computational and experimental techniques to decipher neuronal heterogeneity Here, Andreas Pfenning (Carnegie Mellon University, PA, USA) shares the…
