Matteo Aldeghi

University of Toronto — Canada

Computer Science · Physical Sciences

27h-index4.9kcitations73works8.22yr avg
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Research Topics

Computational Drug Discovery Methods(35), Machine Learning in Materials Science(19), Protein Structure and Dynamics(17), Protein Degradation and Inhibitors(10), Chemical Synthesis and Analysis(9)

Publications73 total

Practically Significant Method Comparison Protocols for Machine Learning in Small Molecule Drug Discovery
Journal of Chemical Information and Modeling·2025· 21 citedOpen Access
Machine learning-assisted exploration of multidrug-drug administration regimens for organoid arrays
Science Advances·2025· 5 citedOpen Access
Author response for "Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation"
2025
Author response for "Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation"
2025
Author response for "Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation"
2025
Prospective Evaluation of Structure-Based Simulations Reveal Their Ability to Predict the Impact of Kinase Mutations on Inhibitor Binding
The Journal of Physical Chemistry B·2025· 9 citedOpen Access
Atlas: a brain for self-driving laboratories
Digital Discovery·2025· 18 citedOpen Access
Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation
Digital Discovery·2025· 8 citedOpen Access
Prospective evaluation of structure-based simulations reveal their ability to predict the impact of kinase mutations on inhibitor binding
2024· 3 citedOpen Access
Practically significant method comparison protocols for machine learning in small molecule drug discovery.
2024· 10 citedOpen Access
Practically significant method comparison protocols for machine learning in small molecule drug discovery.
2024· 4 citedOpen Access
A call for an industry-led initiative to critically assess machine learning for real-world drug discovery
Nature Machine Intelligence·2024· 20 cited
Anubis: Bayesian optimization with unknown feasibility constraints for scientific experimentation
2023· 10 citedOpen Access
Atlas: A Brain for Self-driving Laboratories
2023· 24 citedOpen Access
Olympus, enhanced: benchmarking mixed-parameter and multi-objective optimization in chemistry and materials science
2023· 14 citedOpen Access
Machine learning models to accelerate the design of polymeric long-acting injectables
Nature Communications·2023· 182 citedOpen Access
Machine Learning Models to Accelerate the Design of Polymeric Long-Acting Injectables
Zenodo (CERN European Organization for Nuclear Research)·2022Open Access
On scientific understanding with artificial intelligence
Nature Reviews Physics·2022· 324 citedOpen Access
Machine Learning Models to Accelerate the Design of Polymeric Long-Acting Injectables
2022· 1 citedOpen Access
Roughness of Molecular Property Landscapes and Its Impact on Modellability
Journal of Chemical Information and Modeling·2022· 43 cited
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Frequent Co-authors

Alán Aspuru‐Guzik(13), Bert L. de Groot(13), Riley J. Hickman(11), Vytautas Gapsys(11), Alexander Heifetz(9), Philip C. Biggin(9), Florian Häse(8), Pauric Bannigan(6), Zeqing Bao(6), Christine Allen(6), Mike J. Bodkin(6), Stefan Knapp(5), David L. Mobley(5), Dmitri G. Fedorov(5), Michael J. Bodkin(4), Yuriy Khalak(4), Ewa I. Chudyk(4), Connor W. Coley(4), Iñaki Morao(4), Tim James(4)