11 papers · ranked by Valyu relevance
Maria H. Rasmussen, Jan H. Jensen
We test our meta-molecular dynamics (MD) based approach for finding low-barrier (<30 kcal/mol) reactions (SciPost Chem. 2021, 1, 003) on uni- and bimolecular reactions extracted from the barrier dataset developed by Grambow et al. (Scientific Data 2020, 7, 137). For unimolecular reactions the meta-MD simulations…
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Meta-GGA density functional theory (DFT) is an important method in ab initio materials modelling; however, its computational cost limits applicability for generating large datasets or simulating extended length and time scales, as necessary for modern materials discovery. Deorbitalization is a promising strategy to…
Maria Harris Rasmussen, Julius Seumer, Jan Halborg Jensen
Very recently our group has demonstrated how a genetic algorithm (GA) starting from random tertiary amines can be used to discover a new and efficient catalyst for the alcohol-mediated Morita- Baylis-Hillman (MBH) reaction. In particular, the discovered catalyst was shown experimentally to be eight times more active…
Hosein Fooladi, Steffen Hirte, Johannes Kirchmair
Today, machine learning methods are widely employed in drug discovery. However, the chronic lack of data continues to hamper their further development, validation, and application. Several modern strategies aim to mitigate the challenges associated with data scarcity by learning from data on related tasks. These…
Daniel Gaissmaier, Matthias van den Borg, Donato Fantauzzi, Timo Jacob
In this work, we demonstrate the superior exploration capabilities of the population-based methods over the sequential one-parameter parabolic interpolation (SOPPI) approach to optimise ReaxFF force field parameters. Evolutionary algorithms (EAs) are heuristic-based approaches using a population of concurrent models in…
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Identifying synthesis routes from knowledge graphs poses challenges beyond retrosynthesis, including path–finding artifacts and data issues. We introduce “SynGPS”, a novel algorithm that overcomes these limitations by identifying viable routes even with common artifacts. SynGPS can resolve nonsensical cycles…
Jonas Verhellen
Computer-assisted design of small molecules has experienced a resurgence in academic and indus- trial interest due to the widespread use of data-driven techniques such as deep generative models. While the ability to generate molecules that fulfill required chemical properties is encouraging, the use of deep learning…
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Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
Riley Hickman, Matteo Aldeghi, Alán Aspuru-Guzik
Model-based optimization strategies, such as Bayesian optimization (BO), have been deployed across the natural sciences in design and discovery campaigns due to their sample efficiency and flexibility. The combination of such strategies with automated laboratory equipment and/or high-performance computing in a…
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Metabolite identification studies are an essential but costly and time-consuming component of drug development. Computational methods have the potential to accelerate early-stage drug discovery, particularly with recent advances in deep learning which offer new opportunities to accelerate the process of metabolite…
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The ruthenium-catalyzed meta-C−H alkylation is an increasingly powerful strategy in the arena of remote C−H activation. Currently, the synthesis utility of this strategy is compromised owing to its relatively narrow substrate scope and the challenging diversification of the alkylation products. To address these issues…