Data enhanced Hammett-equation: reaction barriers in chemical space

Bragato, Marco; Rudorff, Guido Falk von; Lilienfeld, O. Anatole von

doi:10.1039/d0sc04235h

Cited by 40 publications

(44 citation statements)

References 128 publications

(172 reference statements)

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“…Solid line shows linear regression (R 2 = 0.97). For disubstituted compound 5, the σ of the substituent was doubled [44][45]. Error bars show ± s.d.…”

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confidence: 99%

Systematic Tuning of Rhodamine Spirocyclization for Super-Resolution Microscopy

Lardon

Wang

Tschanz

et al. 2021

Preprint

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Rhodamines are the most important class of fluorophores for applications in live-cell fluorescence microscopy. This is mainly because rhodamines exist in a dynamic equilibrium between a fluorescent zwitterion and a non-fluorescent but cell-permeable spirocyclic form. Different imaging applications require different positions of this dynamic equilibrium, which poses a challenge for the design of suitable probes. We describe here how the conversion of the ortho-carboxy moiety of a given rhodamine into substituted acyl benzenesulfonamides and alkylamides permits the systematic tuning of the equilibrium of spirocyclization with unprecedented accuracy and over a large range. This allows to transform the same rhodamine into either a highly fluorogenic and cell-permeable probe for live-cell stimulated emission depletion (STED) microscopy, or into a spontaneously blinking dye for single molecule localization microscopy (SMLM). We used this approach to generate differently colored probes optimized for different labeling systems and imaging applications.

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“…Solid line shows linear regression (R 2 = 0.97). For disubstituted compound 5, the σ of the substituent was doubled [44][45]. Error bars show ± s.d.…”

mentioning

confidence: 99%

Systematic Tuning of Rhodamine Spirocyclization for Super-Resolution Microscopy

Lardon

Wang

Tschanz

et al. 2021

Preprint

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“…Such nonvanishing improvement appears to turn into vanishing improvement when employing Δ-ML in order to correct low-quality or coarse-grained baselines, such as a semiempirical PM7 258 or Hammett’s relation. 213 …”

Section: Regressormentioning

confidence: 99%

Ab Initio Machine Learning in Chemical Compound Space

2021

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Chemical compound space (CCS), the set of all theoretically conceivable combinations of chemical elements and (meta-)stable geometries that make up matter, is colossal. The first-principles based virtual sampling of this space, for example, in search of novel molecules or materials which exhibit desirable properties, is therefore prohibitive for all but the smallest subsets and simplest properties. We review studies aimed at tackling this challenge using modern machine learning techniques based on (i) synthetic data, typically generated using quantum mechanics based methods, and (ii) model architectures inspired by quantum mechanics. Such Quantum mechanics based Machine Learning (QML) approaches combine the numerical efficiency of statistical surrogate models with an ab initio view on matter. They rigorously reflect the underlying physics in order to reach universality and transferability across CCS. While state-of-the-art approximations to quantum problems impose severe computational bottlenecks, recent QML based developments indicate the possibility of substantial acceleration without sacrificing the predictive power of quantum mechanics.

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“…quantum machine learning [99][100][101][102][103] (QML), allows navigating chemical compound space (CCS) with high efficiency and precision. ML has become a popular avenue to material science with applications to atomization energies 104,105 , crystal formation energies 106 , carbenes 107 , excited states 108,109 , oxidation states 110 , nuclear magnetic resonance spectra 111 , reaction barriers 112,113 , magnetic systems 114 and charge transfer 115 or molecular fragments 116,117 . Many recent approaches are based on individual levels of CCS, i.e.…”

Section: B Machine Learningmentioning

confidence: 99%

Ab initio machine learning of phase space averages

Weinreich,

Lemm,

von Rudorff

et al. 2022

Preprint

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Equilibrium structures determine material properties and biochemical functions. We propose to machine learn phase-space averages, conventionally obtained by ab initio or force-field based molecular dynamics (MD) or Monte Carlo simulations. In analogy to ab initio molecular dynamics (AIMD), our ab initio machine learning (AIML) model does not require bond topologies and therefore enables a general machine learning pathway to ensemble properties throughout chemical compound space. We demonstrate AIML for predicting Boltzmann averaged structures after training on hundreds of MD trajectories. AIML output is subsequently used to train machine learning models of free energies of solvation using experimental data, and reaching competitive prediction errors (MAE ∼ 0.8 kcal/mol) for out-of-sample molecules-within milli-seconds. As such, AIML effectively bypasses the need for MD or MC-based phase space sampling, enabling exploration campaigns throughout CCS at a much accelerated pace. We contextualize our findings by comparison to state-of-the-art methods resulting in a Pareto plot for the free energy of solvation predictions in terms of accuracy and time.

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Data enhanced Hammett-equation: reaction barriers in chemical space

Abstract: It is intriguing how the Hammett equation enables control of chemical reactivity throughout chemical space by separating the effect of substituents from chemical process variables, such as reaction mechanism, solvent,...

Cited by 40 publications

References 128 publications

Systematic Tuning of Rhodamine Spirocyclization for Super-Resolution Microscopy

Systematic Tuning of Rhodamine Spirocyclization for Super-Resolution Microscopy

Ab Initio Machine Learning in Chemical Compound Space

Ab initio machine learning of phase space averages

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