Molecular design for cardiac cell differentiation using a small dataset and decorated shape features

TL;DR

This paper presents a data-driven approach using decorated shape features and simple regression models to design new compounds for cardiac cell differentiation, effective even with limited training data.

Contribution

It introduces decorated shape descriptors and a conservative molecular design strategy for small datasets, enabling effective compound discovery for stem cell differentiation.

Findings

Improved model performance with decorated shape features

Successful design of a new cardiomyocyte-inducing compound

Experimental validation confirms compound efficacy

Abstract

The discovery of small organic compounds for inducing stem cell differentiation is a time- and resource-intensive process. While data science could, in principle, facilitate the discovery of these compounds, novel approaches are required due to the difficulty of acquiring training data from large numbers of example compounds. In this paper, we demonstrate the design of a new compound for inducing cardiomyocyte differentiation using simple regression models trained with a data set containing only 80 examples. We introduce decorated shape descriptors, an information-rich molecular feature representation that integrates both molecular shape and hydrophilicity information. These models demonstrate improved performance compared to ones using standard molecular descriptors based on shape alone. Model overtraining is diagnosed using a new type of sensitivity analysis. Our new compound is designed using a conservative molecular design strategy, and its effectiveness is confirmed through expression profiles of cardiomyocyte-related marker genes using real-time polymerase chain reaction experiments on human iPS cell lines. This work demonstrates a viable data-driven strategy for designing new compounds for stem cell differentiation protocols and will be useful in situations where training data is limited.