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MF-PCBA: Multifidelity High-Throughput Screening Benchmarks for Drug Discovery and Machine Learning.

David Buterez¹, Jon Paul Janet², Steven J Kiddle³

¹Department of Computer Science and Technology, University of Cambridge, Cambridge CB3 0FD, U.K.

Journal of Chemical Information and Modeling

|April 14, 2023

Summary

High-throughput screening (HTS) generates noisy primary and accurate confirmatory data. We introduce MF-PCBA, a dataset integrating these multifidelity measurements to improve drug discovery models.

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Area of Science:

Computational chemistry and cheminformatics
Drug discovery and development
Machine learning and artificial intelligence

Background:

High-throughput screening (HTS) is crucial for identifying drug candidates, relying on large compound libraries and activity measurements.
Current machine learning datasets often ignore noisy primary screening data, limiting predictive accuracy and experimental design.
Leveraging multiple data modalities from HTS can enhance drug activity predictions and optimize experimental strategies.

Purpose of the Study:

To introduce the Multifidelity PubChem BioAssay (MF-PCBA) dataset, incorporating both primary and confirmatory screening data.
To address the limitation of existing datasets that fail to utilize all available HTS data modalities.
To present a challenging machine learning task focused on integrating low- and high-fidelity screening measurements.

Main Methods:

Acquisition and curation of 60 datasets from PubChem, including primary and confirmatory screening data.
Development of the MF-PCBA dataset, reflecting real-world HTS conventions with 'multifidelity' data.
Evaluation of a deep learning method for multifidelity integration on the MF-PCBA datasets.

Main Results:

The MF-PCBA dataset comprises over 16.6 million unique molecule-protein interactions across 60 datasets.
Integration of primary and confirmatory screening data significantly benefits machine learning models for HTS.
Demonstrated the utility of multifidelity data integration for improved drug activity prediction.

Conclusions:

The MF-PCBA dataset provides a valuable resource for advancing machine learning in drug discovery by utilizing all HTS data modalities.
Multifidelity data integration represents a promising approach for more accurate and cost-effective drug candidate identification.
Further research into molecular activity landscape roughness can optimize computational drug discovery efforts.