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A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios

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Abstract

We release a new, high quality data set of 1162 PDE10A inhibitors with experimentally determined binding affinities together with 77 PDE10A X-ray co-crystal structures from a Roche legacy project. This data set is used to compare the performance of different 2D- and 3D-machine learning (ML) as well as empirical scoring functions for predicting binding affinities with high throughput. We simulate use cases that are relevant in the lead optimization phase of early drug discovery. ML methods perform well at interpolation, but poorly in extrapolation scenarios—which are most relevant to a real-world application. Moreover, we find that investing into the docking workflow for binding pose generation using multi-template docking is rewarded with an improved scoring performance. A combination of 2D-ML and 3D scoring using a modified piecewise linear potential shows best overall performance, combining information on the protein environment with learning from existing SAR data.

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Data availability

Code for the docking workflow is available at https://github.com/ccdc-opensource/science-paper-rf-machine-learned-scoring-2022.git.

Code availability

Code to calculate RF values is available at https://github.com/jasonccole/relative-interaction-frequencies. Docking poses are available at https://doi.org/10.6084/m9.figshare.20055014.

Abbreviations

ACNN:

Atomic convolutional neural network

LO:

Lead optimization

MCS:

Maximum common substructure

PDE10A:

Phosphodiesterase 10A

RF:

Ratio of frequencies

SAR:

Structure-activity relationship

tSNE:

T-distributed stochastic neighbor embedding

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Acknowledgements

We thank Robin Taylor for valuable feedback and review of the manuscript. Fabian Dey is acknowledged for providing code to align molecules. We also thank Jörg Benz and Catherine Joseph for PDE10A crystallization. Finally, the data set would not exist without the great synthetic efforts of Konrad Bleicher, Luca Gobbi, Katrin Groebke-Zbinden, Matthias Koerner, Christian Lerner, Jens-Uwe Peters, Rosa Maria Rodriguez-Sarmiento, and associated lab members.

Funding

No funding was received for conducting this study.

Author information

Authors and Affiliations

  1. Roche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Grenzacherstrasse 124, CH-4070, Basel, Switzerland

    Andreas Tosstorff, Markus G. Rudolph, Michael Reutlinger, Christian Kramer, Hervé Schaffhauser, Agnès Nilly, Alexander Flohr & Bernd Kuhn

  2. Cambridge Crystallographic Data Centre, 12 Union Road, Cambridge, CB2 1EZ, UK

    Jason C. Cole

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  1. Andreas Tosstorff
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Contributions

AT and BK: designed the study and wrote the manuscript. JC, CK, MiR: provided input to the design of the study and reviewed the manuscript. MaR: determined all crystal structures of the study and reviewed the manuscript. AF: synthesized compounds and supervised the chemistry program. HS and AN: generated and analysed binding data. AT, BK, JC: wrote code used for docking, scoring and data analysis.

Corresponding author

Correspondence to Andreas Tosstorff.

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Conflict of interest

JC works for the Cambridge Crystallographic Data Centre which develops the GOLD docking software.

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Tosstorff, A., Rudolph, M.G., Cole, J.C. et al. A high quality, industrial data set for binding affinity prediction: performance comparison in different early drug discovery scenarios. J Comput Aided Mol Des 36, 753–765 (2022). https://doi.org/10.1007/s10822-022-00478-x

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