High-throughput screening (HTS), as one of the key techniques
in
drug discovery, is frequently used to identify promising drug candidates
in a largely automated and cost-effective way. One of the necessary
conditions for successful HTS campaigns is a large and diverse compound
library, enabling hundreds of thousands of activity measurements per
project. Such collections of data hold great promise for computational
and experimental drug discovery efforts, especially when leveraged
in combination with modern deep learning techniques, and can potentially
lead to improved drug activity predictions and cheaper and more effective
experimental design. However, existing collections of machine-learning-ready
public datasets do not exploit the multiple data modalities present
in real-world HTS projects. Thus, the largest fraction of experimental
measurements, corresponding to hundreds of thousands of “noisy”
activity values from primary screening, are effectively ignored in
the majority of machine learning models of HTS data. To address these
limitations, we introduce Multifidelity PubChem BioAssay (MF-PCBA),
a curated collection of 60 datasets that includes two data modalities
for each dataset, corresponding to primary and confirmatory screening,
an aspect that we callmultifidelity. Multifidelity
data accurately reflect real-world HTS conventions and present a new,
challenging task for machine learning: the integration of low- and
high-fidelity measurements through molecular representation learning,
taking into account the orders-of-magnitude difference in size between
the primary and confirmatory screens. Here we detail the steps taken
to assemble MF-PCBA in terms of data acquisition from PubChem and
the filtering steps required to curate the raw data. We also provide
an evaluation of a recent deep-learning-based method for multifidelity
integration across the introduced datasets, demonstrating the benefit
of leveraging all HTS modalities, and a discussion in terms of the
roughness of the molecular activity landscape. In total, MF-PCBA contains
over 16.6 million unique molecule–protein interactions. The
datasets can be easily assembled by using the source code available
athttps://github.com/davidbuterez/mf-pcba.