Raimon Fabregat I De Aguilar-Amat

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How Robust Is the Reversible Steric Shielding Strategy for Photoswitchable Organocatalysts?

Raimon Fabregat I De Aguilar-Amat, Simone Gallarati, Théo Pierre Jaffrelot Inizan, Veronika Juraskova

A highly appealing strategy to modulate a catalyst's activity and/or selectivity in a dynamic and noninvasive way is to incorporate a photoresponsive unit into a catalytically competent molecule. However, the description of the photoinduced conformational or structural changes that alter the catalyst's intrinsic reactivity is often reduced to a handful of intuitive static representations, which can struggle to capture the complexity of flexible organocatalysts. Here, we show how a comprehensive exploration of the free energy landscape of N-alkylated azobenzene-tethered piperidine catalysts is essential to unravel the conformational characteristics of each configurational state and explain the experimentally observed reactivity trends. Mapping the catalysts' conformational space highlights the existence of false ON or OFF states that lower their switching ability. Our findings expose the challenges associated with the realization of a reversible steric shielding for the photocontrol of Bronsted basicity of piperidine photoswitchable organocatalysts.

AMER CHEMICAL SOC2022

Kernel Methods and Similarity Learning Applied in Computational Chemistry

Raimon Fabregat I De Aguilar-Amat

Over the last two decades, data-powered machine learning (ML) tools have profoundly transformed numerous scientific fields. In computational chemistry, machine learning applications have permitted faster predictions of chemical properties and provided powerful analytical tools, facilitating the exploration of the chemical space. The original work presented in this thesis leverages the paradigm-shifting influence of ML and focuses on bridging the divide between unsupervised and supervised learning with the overarching objective of improving the predictive power of similarity-based machine learning algorithms such as kernel regression.Despite their widespread use in chemistry, current implementations of kernel regression suffer from biased definitions of similarity between chemical environments. This problem originates from the rigidity of current numerical approaches for encoding molecular information, based on expert-crafted representations. Moreover, it is amplified by the incorrect (yet generalized) assumption that increasing the amount of information encoded in molecular representations unequivocally improves the evaluation of molecular similarity. As a result, the performance of kernel models can be sub-optimal reducing their broad applicability. To overcome such limitations, we introduce a series of statistical tools and methodologies based on supervised dimensionality reduction and metric learning capable of filtering and adapting the features of common molecular representations. This allows tailoring the notion of "molecular similarity" in order to optimize the prediction of specific chemical targets.Using examples such as the exploration of the free-energy landscape of oligopeptides or the prediction of subtle properties associated with the outcome of chemical reactions (for example, enantiomeric excess), we demonstrate how the methods proposed in this thesis unlock the optimal performance of kernel regression and, more generally, of any similarity-based algorithm. Overall, the work within is part of a larger, more comprehensive effort aimed at extending the capabilities of computational modeling to increasingly complex chemical situations by exploiting the latest advances in statistical learning.

EPFL2022

Local Kernel Regression and Neural Network Approaches to the Conformational Landscapes of Oligopeptides

Michele Ceriotti, Edgar Albert Engel, Raimon Fabregat I De Aguilar-Amat, Alberto Fabrizio, Veronika Juraskova, Benjamin André René Meyer

The application of machine learning to theoretical chemistry has made it possible to combine the accuracy of quantum chemical energetics with the thorough sampling of finite-temperature fluctuations. To reach this goal, a diverse set of methods has been proposed, ranging from simple linear models to kernel regression and highly nonlinear neural networks. Here we apply two widely different approaches to the same, challenging problem: the sampling of the conformational landscape of polypeptides at finite temperature. We develop a local kernel regression (LKR) coupled with a supervised sparsity method and compare it with a more established approach based on Behler-Parrinello type neural networks. In the context of the LKR, we discuss how the supervised selection of the reference pool of environments is crucial to achieve accurate potential energy surfaces at a competitive computational cost and leverage the locality of the model to infer which chemical environments are poorly described by the DFTB baseline. We then discuss the relative merits of the two frameworks and perform Hamiltonian-reservoir replica-exchange Monte Carlo sampling and metadynamics simulations, respectively, to demonstrate that both frameworks can achieve converged and transferable sampling of the conformational landscape of complex and flexible biomolecules with comparable accuracy and computational cost.

2022