Protein-ligand data at scale to support machine learning
Review article, 2025
Target 2035 is a global initiative that aims to develop a potent and selective pharmacological modulator, such as a chemical probe, for every human protein by 2035. Here, we describe the Target 2035 roadmap to develop computational methods to improve small-molecule hit discovery, which is a key bottleneck in the discovery of chemical probes. Large, publicly available datasets of high-quality protein-small-molecule binding data will be created using affinity-selection mass spectrometry and DNA-encoded chemical library screening. Positive and negative data will be made openly available, and the machine learning community will be challenged to use these data to build models and predict new, diverse small-molecule binders. Iterative cycles of prediction and testing will lead to improved models and more successful predictions. By 2030, Target 2035 will have identified experimentally verified hits for thousands of human proteins and advanced the development of open-access algorithms capable of predicting hits for proteins for which there are not yet any experimental data.
Author
Aled M. Edwards
University of Toronto
Dafydd R. Owen
Pfizer
Leili Zhang
IBM Research
Damian W. Young
Baylor College of Medicine
Timothy M. Willson
The University of North Carolina System
James Wellnitz
The University of North Carolina System
Yanli Wang
National Institutes of Health
Jarrod Walsh
AstraZeneca AB
Erik Vernet
Novo Nordisk
Alexander Tropsha
The University of North Carolina System
Claudia Tredup
Goethe University Frankfurt
Matthew H. Todd
University College London (UCL)
Amelia Tjaden
Goethe University Frankfurt
Sven Thamm
Boehringer Ingelheim
Michael Sundström
Karolinska Institutet
Karolinska University Hospital
Andreas Steffen
Pfizer
Shaun Stauffer
Cleveland Clinic Foundation
Lucas Rodrigo de Souza
State University of Campinas
Min Shen
National Institutes of Health
Kristof T. Schütt
Pfizer
Lovisa Holmberg Schiavone
AstraZeneca AB
Matthieu Schapira
University of Toronto
Santha Santhakumar
University of Toronto
Kumar Saikatendu
Takeda Pharmaceuticals
Emma Rivers
AstraZeneca AB
Dušan Petrović
Nuvisan
Hui Peng
University of Toronto
John P. O’Donnell
Bayer AG
Susanne Müller-Knapp
Goethe University Frankfurt
Anke Mueller-Fahrnow
Nuvisan
Maxwell R. Morgan
University of Toronto
Florian Montel
Boehringer Ingelheim
Juan Carlos Mobarec
AstraZeneca AB
Maurice Michel
Karolinska Institutet
Karolinska University Hospital
Sofia Melliou
University of Toronto
Uta Lessel
Boehringer Ingelheim
Andrew R. Leach
Wellcome Trust
Oliver Krämer
Boehringer Ingelheim
Florian Krieger
Evotec
Stefan Knapp
Goethe University Frankfurt
Anthony D. Keefe
X-Chem
Aimo Kannt
Fraunhofer Society
Scott A. Johnson
Bristol-Myers Squibb
Sandra Häberle
Goethe University Frankfurt
Emily Rose Holzinger
Bristol-Myers Squibb
Ingo V. Hartung
Merck KGaA
Rachel J. Harding
University of Toronto
Thomas Hanke
Goethe University Frankfurt
Levon Halabelian
University of Toronto
Benjamin Haibe-Kains
University of Toronto
Judith Günther
Bayer AG
Marie-Aude Guié
X-Chem
Claudia Gordijo
University of Toronto
Opher Gileadi
Karolinska University Hospital
Karolinska Institutet
Luca Foschini
Sage Bionetworks
Amaury Fernández-Montalván
Boehringer Ingelheim
Ola Engkvist
AstraZeneca AB
University of Gothenburg
Chalmers, Computer Science and Engineering (Chalmers), Data Science and AI
Madison M. Edwards
University of Toronto
Katharina Duerr
OMass Therapeutics
David Drewry
The University of North Carolina System
Dengfeng Dou
HitGen
Snezana Djordjevic
University College London (UCL)
Alejandra Solache Diaz
Abcam plc
Sergio Martinez Cuesta
AstraZeneca AB
Rafael Counago
The University of North Carolina System
Wendy D. Cornel
IBM Research
Jesse A. Coker
Cleveland Clinic Foundation
Djork Arné Clevert
Pfizer
Timothy Cernak
University of Michigan
Nicola A. Burgess-Brown
University College London (UCL)
Peter Brown
The University of North Carolina System
Mario Henrique Bengtson
State University of Campinas
Frances M. Bashore
The University of North Carolina System
Dalia Barsyte-Lovejoy
University of Toronto
Arrash J. Baghaie
X-Chem
Alison D. Axtman
The University of North Carolina System
Cheryl Arrowsmith
University of Toronto
Albert A. Antolin
Hospital Duran i Reynals
Suzanne Ackloo
University of Toronto
NATURE REVIEWS CHEMISTRY
2397-3358 (eISSN)
Vol. In PressSubject Categories (SSIF 2025)
Bioinformatics (Computational Biology)
Bioinformatics and Computational Biology
Medicinal Chemistry
DOI
10.1038/s41570-025-00737-z
PubMed
40702244