383 citations · 976 across the 8 of their papers we have counts for
9 papers
Roughness of molecular property landscapes and its impact on modellability
Matteo Aldeghi, David E. Graff, Nathan Frey +4
In molecular discovery and drug design, structure-property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemi…
A graph representation of molecular ensembles for polymer property prediction
Matteo Aldeghi, Connor W. Coley
Synthetic polymers are versatile and widely used materials. Similar to small organic molecules, a large chemical space of such materials is hypothetically accessible. Computational…
Self-focusing virtual screening with active design space pruning
David E. Graff, Matteo Aldeghi, Joseph A. Morrone +3
High-throughput virtual screening is an indispensable technique utilized in the discovery of small molecules. In cases where the library of molecules is exceedingly large, the cost…
On scientific understanding with artificial intelligence
Mario Krenn, Robert Pollice, Si Yue Guo +9
Imagine an oracle that correctly predicts the outcome of every particle physics experiment, the products of every chemical reaction, or the function of every protein. Such an oracl…
Bayesian optimization with known experimental and design constraints for chemistry applications
Riley J. Hickman, Matteo Aldeghi, Florian Häse +1
Optimization strategies driven by machine learning, such as Bayesian optimization, are being explored across experimental sciences as an efficient alternative to traditional design…
Golem: An algorithm for robust experiment and process optimization
Matteo Aldeghi, Florian Häse, Riley J. Hickman +2
Numerous challenges in science and engineering can be framed as optimization tasks, including the maximization of reaction yields, the optimization of molecular and materials prope…
Assigning Confidence to Molecular Property Prediction
AkshatKumar Nigam, Robert Pollice, Matthew F. D. Hurley +6
Introduction: Computational modeling has rapidly advanced over the last decades, especially to predict molecular properties for chemistry, material science and drug design. Recentl…
Olympus: a benchmarking framework for noisy optimization and experiment planning
Florian Häse, Matteo Aldeghi, Riley J. Hickman +5
Research challenges encountered across science, engineering, and economics can frequently be formulated as optimization tasks. In chemistry and materials science, recent growth in…
Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge
Florian Häse, Matteo Aldeghi, Riley J. Hickman +2
Designing functional molecules and advanced materials requires complex design choices: tuning continuous process parameters such as temperatures or flow rates, while simultaneously…