Home Community MIT Researchers Unveil InfoCORE: A Machine Learning Approach to Overcome Batch Effects in High-Throughput Drug Screening

MIT Researchers Unveil InfoCORE: A Machine Learning Approach to Overcome Batch Effects in High-Throughput Drug Screening

MIT Researchers Unveil InfoCORE: A Machine Learning Approach to Overcome Batch Effects in High-Throughput Drug Screening

Recent studies have shown that representation learning has turn out to be a crucial tool for drug discovery and biological system understanding. It’s a fundamental component within the identification of drug mechanisms, the prediction of drug toxicity and activity, and the identification of chemical compounds linked to disease states.

The limitation arises in representing the complex interplay between a small molecule’s chemical structure and its physical or biological characteristics. Several molecular representation learning techniques currently in use solely encode a molecule’s chemical identification, resulting in unimodal representations, which has drawbacks as molecules with comparable structures can have remarkably diverse functions inside a biological setting.

Recent efforts have targeting training models that apply multimodal contrastive learning to map 2D chemical structures to high-content cell microscope pictures. In biotechnology, high-throughput drug screening is important for assessing and understanding the connection between a drug’s chemical structure and biological activity. This method uses gene expression measures or cell imaging to point drug effects. 

Nevertheless, handling batch effects presents a significant challenge when running large-scale screens, necessitating their division into many trials. The suitable interpretation of results could also be hampered by these batch effects, which may potentially incorporate systematic mistakes and non-biological connections into the information. 

To beat this, a team of researchers has recently presented InfoCORE, an Information maximization strategy for COnfounder REmoval. Effectively managing batch effects and improving the caliber of molecular representations derived from high-throughput drug screening data are the foremost goals of InfoCORE. Given a batch identifier, the strategy sets a variational lower sure on the conditional mutual information of latent representations. It does this by adaptively reweighting samples to equalize their inferred batch distribution.

Extensive tests on drug screening data have shown that InfoCORE performs higher than other algorithms on quite a lot of tasks, equivalent to retrieving molecule-phenotype and predicting chemical properties. This suggests that InfoCORE successfully reduces the influence of batch effects, leading to higher performance in tasks pertaining to molecular evaluation and drug discovery.

The study has also emphasized on how flexible InfoCORE is as a framework that may handle more complex issues. It has shown how InfoCORE can manage shifts in the final distribution and data fairness problems by reducing correlation with bogus characteristics or eliminating sensitive attributes. InfoCORE’s versatility makes it a strong tool for tackling quite a lot of challenges connected to data distribution and fairness, along with removing the batch effect in drug screening.

The researchers have summarized their primary contributions as follows.

  1. The InfoCORE approach goals to propose a multimodal molecular representation learning framework that may easily integrate chemical structures with quite a lot of high-content drug screens.
  1. The research provides a powerful theoretical foundation by demonstrating that InfoCORE maximizes the variational lower sure on the conditional mutual information of the representation given the batch identifier.
  1. InfoCORE has demonstrated its efficiency in molecular property prediction and molecule-phenotype retrieval tasks by consistently outperforming several baseline models in real-world studies.
  1. InfoCORE’s information maximization philosophy extends beyond the sector of drug development. Empirical evidence supports its effectiveness in removing sensitive information for representation fairness, making it a versatile tool with wider uses.

Take a look at the Paper and Github. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter. Join our 36k+ ML SubReddit, 41k+ Facebook Community, Discord Channel, and LinkedIn Group.

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Tanya Malhotra is a final yr undergrad from the University of Petroleum & Energy Studies, Dehradun, pursuing BTech in Computer Science Engineering with a specialization in Artificial Intelligence and Machine Learning.
She is a Data Science enthusiast with good analytical and significant considering, together with an ardent interest in acquiring latest skills, leading groups, and managing work in an organized manner.

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