CLAMP: A Novel Architecture for Activity Prediction Using Deep Learning

Predicting a Molecule’s Properties: A Breakthrough in AI Research

For years, scientists have been working on predicting the properties of molecules based on their chemical structures. Recent technological advancements have allowed machine learning algorithms to uncover correlations between the structure and characteristics of these molecules. Deep learning has introduced activity prediction models, which are crucial in the computational drug discovery industry.

Activity prediction models use various chemical structure descriptions, like chemical fingerprints, descriptors, molecular graphs, or the SMILES representation. While these models have made significant progress, they haven’t reached the same level of advancement as vision and language models.

Training these activity prediction models requires annotated data from biological experiments, which is time-consuming and labor-intensive. Researchers are eager to find methods that can efficiently train these models with fewer data points. Additionally, current models struggle to use comprehensive information about the prediction tasks, mainly due to the lack of measurement data.

To address these challenges, researchers from the Machine Learning Department at Johannes Kepler University Linz, Austria, developed a novel architecture called CLAMP. This architecture pre-trains a separate molecule and language encoder using chemical databases as training or pre-training data. The researchers also propose a contrastive pre-training objective that leverages the vast amount of chemical structures in databases.

CLAMP utilizes a trainable text encoder to create bioassay embeddings and a trainable molecule encoder to create molecule embeddings. It also includes a scoring function that determines the activity level of a molecule on a bioassay. Through contrastive learning, CLAMP achieves zero-shot transfer learning, providing insightful predictions for unseen bioassays.

Experimental evaluations have shown that CLAMP significantly improves predictive performance for few-shot and zero-shot learning in drug discovery tasks. The modular architecture and pre-training objective contribute to its remarkable performance. However, there is still room for improvement, as certain elements like chemical dosage are not considered in the predictions.

Despite its limitations, CLAMP has outperformed other methods in zero-shot prediction drug discovery tasks. To learn more about this research, check out the Paper and Github.

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