Enhanced Sampling Aided Design of Molecular Photoswitches

Development of Parallel On-the-Fly Crystal Algorithm for Reaction Discovery in Large and Complex Molecular Systems

The parallel on-the-fly Crystal algorithm is a new, efficient global search algorithm for exploring single-state potential energy surfaces and conical intersection seam spaces of a wide range of molecules. Despite major developments, its application to complex molecular systems, especially in the condensed phase, remains challenging due to the high dimensionality of the configurational space. In this work, we address this challenge and extend its applicability to the reaction discovery of large and complex molecular photoswitches in various molecular environments, including in the condensed phase with explicit solvent molecules. This is achieved by performing an explicit exploration of a comparatively large Crystal configurational subspace, while gradually relaxing the remaining degrees of freedom. The new Crystal algorithm is applied to the reaction discovery of bilirubin and donor-acceptor Stenhouse adducts, a next-generation class of molecular photoswitches, in vacuum and in the aqueous solution. To this end, we designed an automated and systematic workflow for Crystal to discover and characterize new minima and low-energy reaction pathways in these challenging and complex systems. Our findings demonstrate the algorithm's effectiveness in quickly exploring the configuration space and uncovering kinetically accessible products, offering new insights into the intricate chemical reactivities of these molecules and the roles of molecular environments on the reaction pathways. The results underscore the promising potential of parallelized global exploration methods for reaction discovery in biomolecular systems.

Predictive modeling of visible-light azo-photoswitches' properties using structural features

In this manuscript we present the strategy for modeling photoswitch properties (maximum absorption wavelength and thermal half-life of photoisomers) of visible-light azo-photoswitches using structural data. We compile a comprehensive data set from literature sources and perform a rigorous benchmark to select the best feature type and modeling approach. The fragment counts have demonstrated the best performance in the benchmark for both properties. We validate the models in cross-validation and on an external set. The predictions of absorption wavelengths for this set are highly accurate; on the other hand, the model for thermal half-life is less reliable, likely due to the modest size of the data set related to half-life of photoisomers, although consensus modeling approach allows to improve the predictivity. We also provide an interpretation of the modeling results using ColorAtom approach and the insights into the chemical space covered by the data set.Scientific contribution The paper provides a machine learning approach based only on structural features to predict two important photoswitch properties. Unlike previous studies, we do not use any quantum chemical features which accelerates the modeling procedure, while the accuracy of models remains high. Moreover, the fragment counts offer unique approach to model interpretation that is useful for rational design of photoswitches with desired properties.

Conceptual expansion of photomedicine for spatiotemporal treatment methods

Photomedicine has evolved from basic phototherapy to a broad range of light-based technologies to achieve precise and minimally invasive therapeutic outcomes. Recent advances in light sources, photochemical reactions, and photoswitches have facilitated the development of light-activated methodologies for modulating biological processes. This review discusses the history of light therapy that leads to the emergence of a new field known as photopharmacology, mode of actions in photopharmacology such as photodynamic, photo-uncaging and photoswitchable methods, a few representative examples in photopharmacology, and a brief overview of its associated challenges. The current developments in photopharmacology hold great promise for the treatment of diseases such as cancer, with enhanced therapeutic precision, and minimal side effects. We foresee further expansion of photomedicine for novel approaches in precision medicine and healthcare, and unprecedented treatment methods.

Acceleration of Diffusion in Ab Initio Nanoreactor Molecular Dynamics and Application to Hydrogen Sulfide Oxidation

The computational description of chemical reactivity can become extremely complex when multiple different reaction products and intermediates come into play, forming a chemical reaction network. Therefore, computational methods for the automated construction of chemical reaction networks have been developed in the last decades. One of these methods, ab initio nanoreactor molecular dynamics (NMD), is based on external forces enhancing reactivity by e.g., periodically compressing the system and allowing it to relax. However, during the relaxation process, a significant simulation time is required to allow energy to dissipate and molecules to diffuse, making this part of the NMD simulation computationally intensive. This work aims to improve NMD by accelerating the diffusion process in the relaxation phase. We systematically investigate the speedup of reaction discovery gained by diffusion acceleration, leading to a factor of up to 28 in discovery frequency. Diffusion-accelerated nanoreactor molecular dynamics (DA-NMD) is then used to construct a reaction network of hydrogen sulfide oxidation under atmospheric conditions, where reactions are automatically detected by a change in the bond order and bond distance. A reaction network of 108 molecular species and 399 elementary reactions was constructed starting from hydrogen sulfide, hydroxy radicals, and molecular oxygen covering a broad variety of sulfur-oxygen chemistry and oxidation states of the sulfur atom ranging from -II to +VI.

Molecular dynamics-guided reaction discovery reveals endoperoxide-to-alkoxy radical isomerization as key branching point in α-pinene ozonolysis

Secondary organic aerosols (SOAs) significantly impact Earth's climate and human health. Although the oxidation of volatile organic compounds (VOCs) has been recognized as the major contributor to the atmospheric SOA budget, the mechanisms by which this process produces SOA-forming highly oxygenated organic molecules (HOMs) remain unclear. A major challenge is navigating the complex chemical landscape of these transformations, which traditional hypothesis-driven methods fail to thoroughly investigate. Here, we explore the oxidation of α-pinene, a critical atmospheric biogenic VOC, using a novel reaction discovery approach based on molecular dynamics and state-of-the-art enhanced sampling techniques. Our approach successfully identifies all established reaction pathways of α-pinene ozonolysis, as well as discovers multiple novel species and pathways without relying on a priori chemical knowledge. In particular, we unveil a key branching point that leads to the rapid formation of alkoxy radicals, whose high and diverse reactivity help to explain hitherto unexplained oxidation pathways suggested by mass spectral peaks observed in α-pinene ozonolysis experiments. This branching point is likely prevalent across a variety of atmospheric VOCs and could be crucial in establishing the missing link to SOA-forming HOMs.

Enhanced Sampling with Suboptimal Collective Variables: Reconciling Accuracy and Convergence Speed

We introduce an enhanced sampling algorithm to obtain converged free energy landscapes of molecular rare events, even when the collective variable (CV) used for biasing is not optimal. Our approach samples a time-dependent target distribution by combining the on-the-fly probability enhanced sampling and its exploratory variant, OPES Explore. This promotes more transitions between the relevant metastable states and accelerates the convergence speed of the free energy estimate. We demonstrate the successful application of this combined algorithm on the two-dimensional Wolfe-Quapp potential, millisecond time-scale ligand-receptor binding in the trypsin-benzamidine complex, and folding-unfolding transitions in chignolin mini-protein. Our proposed algorithm can compute accurate free energies at an affordable computational cost and is robust in terms of the choice of CVs, making it particularly promising for the simulation of complex biomolecular systems.

Compartmentalizing Donor-Acceptor Stenhouse Adducts for Structure-Property Relationship Analysis

The development of photoswitches that absorb low energy light is of notable interest due to the growing demand for smart materials and therapeutics necessitating benign stimuli. Donor-acceptor Stenhouse adducts (DASAs) are molecular photoswitches that respond to light in the visible to near-infrared spectrum. As a result of their modular assembly, DASAs can be modified at the donor, acceptor, triene, and backbone heteroatom molecular compartments for the tuning of optical and photoswitching properties. This Perspective focuses on the electronic and steric contributions at each compartment and how they influence photophysical properties through the adjustment of the isomerization energetic landscape. An emphasis on current synthetic strategies and their limitations highlights opportunities for DASA architecture, and thus photophysical property expansion.

A DASA displaying highly efficient and rapid reversible isomerization within sustainable nano/micro capsules: one step closer to sustainability

Donor-acceptor Stenhouse adducts (DASAs), derived from bio-based furfural, demonstrate reversible isomerization when exposed to light and heat, positioning them as attractive candidates for sustainable smart materials. However, achieving efficient and rapid isomerization in high bio-content solid-state matrices, especially under mild conditions, remains a significant hurdle due to restricted molecular mobility and limited matrix options. To address this, we developed a novel solid matrix in the form of sustainable nano/micro capsules, which boast the highest bio-content reported to date (57%). Composed of polymethylmethacrylate (PMMA) and a lauric-stearic acid eutectic mixture (L-SEM), these capsules facilitate highly efficient and rapid reversible isomerization of a third-generation DASA (DASA-1). Remarkably, the system achieves 84% forward and 90% reverse isomerization under mild temperatures, significantly enhancing the material's photo-switching capabilities. This advancement not only addresses the critical challenge of isomerization within high bio-content solid matrices but also opens broader possibilities for the application of bio-based DASAs in environmentally friendly technologies, such as color-rich rewritable papers. By innovating in the design of sustainable smart materials, this work has the potential to extend the utility of DASAs across various scientific fields, contributing to the global shift towards a low-carbon, environmentally sustainable society.

"On-the-fly" Crystal : How to reliably and automatically characterize and construct potential energy surfaces

In this work, the Crystal  code, developed previously by the authors to find "holes" as well as legitimate transition states in existing potential energy surface (PES) functions [JPC Lett. 11, 6468 (2020)], is retooled to perform on-the-fly "direct dynamics"-type PES explorations, as well as automatic construction of new PES functions. In all of these contexts, the chief advantage of Crystal  over other methods is its ability to globally map the PES, thereby determining the most relevant regions of configuration space quickly and reliably-even when the dimensionality is rather large. Here, Crystal  is used to generate a uniformly spaced grid of density functional theory (DFT) or ab initio points, truncated over the relevant regions, which can then be used to either: (a) hone in precisely on PES features such as minima and transition states, or; (b) create a new PES function automatically, via interpolation. Proof of concept is demonstrated via application to three molecular systems: water (H2 O), (reduced-dimensional) methane (CH4 ), and methylene imine (CH2 NH).

Computing the committor with the committor to study the transition state ensemble

The study of the kinetic bottlenecks that hinder the rare transitions between long-lived metastable states is a major challenge in atomistic simulations. Here we propose a method to explore the transition state ensemble, which is the distribution of configurations that the system passes through as it translocates from one metastable basin to another. We base our method on the committor function and the variational principle that it obeys. We find its minimum through a self-consistent procedure that starts from information limited to the initial and final states. Right from the start, our procedure allows the sampling of very many transition state configurations. With the help of the variational principle, we perform a detailed analysis of the transition state ensemble, ranking quantitatively the degrees of freedom mostly involved in the transition and enabling a systematic approach for the interpretation of simulation results and the construction of efficient physics-informed collective variables.

Robust Red-Absorbing Donor-Acceptor Stenhouse Adduct Photoswitches

Donor-Acceptor Stenhouse Adduct (DASA), a class of push-pull negative photochrome, has received large interest lately owing to its versatile synthesis, modularity and excellent photoswitching in solutions. From a technological perspective, it is imperative for this class of photoswitches to work robustly in solid state, e. g. thin films. We feature a molecular framework for the optimized design of DASAs by introducing a new thioindoline donor (D3) and assessing its performance against known 2nd generation indoline-based donors. The systematic structure-function investigations suggest that to achieve robust reversible photoswitching, a ground state with low charge separation is desired. DASAs with stronger electron donors and a larger charge separation in the ground state result in a low population of the photothermalstationary state (PTSS) and reduced photostability. The DASA with thioindoline donor (D3A3) seems to be a special case among the donor series as it causes a red shift (ca. 15 nm), however with less polarization of the ground state and marginally better photostability as compared to the unsubstituted 2-methyl indoline (D1A3). We also emphasize the consideration of the key additional factors that can modulate the red-light photoswitching properties of DASA chromophores in polymer thin films, which might not be dominant in homogenous solution state.

Automated Exploration of Reaction Networks and Mechanisms Based on Metadynamics Nanoreactor Simulations

We developed an automated approach to construct a complex reaction network and explore the reaction mechanisms for numerous reactant molecules by integrating several theoretical approaches. Nanoreactor-type molecular dynamics was used to generate possible chemical reactions, in which the metadynamics was used to overcome the reaction barriers, and the semiempirical GFN2-xTB method was used to reduce the computational cost. Reaction events were identified from trajectories using the hidden Markov model based on the evolution of the molecular connectivity. This provided the starting points for further transition-state searches at the electronic structure levels of density functional theory to obtain the reaction mechanism. Finally, the entire reaction network containing multiple pathways was built. The feasibility and efficiency of the automated construction of the reaction network were investigated using the HCHO and NH3 biomolecular reaction and the reaction network for a multispecies system comprising dozens of HCN and H2O molecules. The results indicated that the proposed approach provides a valuable and effective tool for the automated exploration of the reaction networks.

Visible light-responsive materials: the (photo)chemistry and applications of donor-acceptor Stenhouse adducts in polymer science

Donor-acceptor Stenhouse adduct (DASA) photoswitches have gained a lot of attention since their discovery in 2014. Their negative photochromism, visible light absorbance, synthetic tunability, and the large property changes between their photoisomers make them attractive candidates over other commonly used photoswitches for use in materials with responsive or adaptive properties. The development of such materials and their translation into advanced technologies continues to widely impact forefront materials research, and DASAs have thus attracted considerable interest in the field of visible-light responsive molecular switches and dynamic materials. Despite this interest, there have been challenges in understanding their complex behavior in the context of both small molecule studies and materials. Moreover, incorporation of DASAs into polymers can be challenging due to their incompatibility with the conditions for most common polymerization techniques. In this review, therefore, we examine and critically discuss the recent developments and challenges in the field of DASA-containing polymers, aiming at providing a better understanding of the interplay between the properties of both constituents (matrix and photoswitch). The first part summarizes current understanding of DASA design and switching properties. The second section discusses strategies of incorporation of DASAs into polymers, properties of DASA-containing materials, and methods for studying switching of DASAs in materials. We also discuss emerging applications for DASA photoswitches in polymeric materials, ranging from light-responsive drug delivery systems, to photothermal actuators, sensors and photoswitchable surfaces. Last, we summarize the current challenges in the field and venture on the steps required to explore novel systems and expand both the functional properties and the application opportunities of DASA-containing polymers.

Tethered together: DASA design towards aqueous compatibility

Donor-acceptor Stenhouse adducts (DASAs) are an exciting class of photoswitches due to their facile tunability, visible light absorbance, and negative photochromism. While they have shown use in a variety of applications, to date all reported DASA derivatives have low equilibrium and/or poor photoswitching in polar protic solvents, which is vital for moving towards applications in biological systems. We demonstrate a strategy to introduce a substitution on the DASA triene that results in derivatives that are stable and have high dark equilibrium of the open form in polar protic solvents. Decreasing the charge separation of these new derivatives also allows for reversible switching in polar and protic solvents including THF : water mixtures.

OneOPES, a Combined Enhanced Sampling Method to Rule Them All

Enhanced sampling techniques have revolutionized molecular dynamics (MD) simulations, enabling the study of rare events and the calculation of free energy differences in complex systems. One of the main families of enhanced sampling techniques uses physical degrees of freedom called collective variables (CVs) to accelerate a system's dynamics and recover the original system's statistics. However, encoding all the relevant degrees of freedom in a limited number of CVs is challenging, particularly in large biophysical systems. Another category of techniques, such as parallel tempering, simulates multiple replicas of the system in parallel, without requiring CVs. However, these methods may explore less relevant high-energy portions of the phase space and become computationally expensive for large systems. To overcome the limitations of both approaches, we propose a replica exchange method called OneOPES that combines the power of multireplica simulations and CV-based enhanced sampling. This method efficiently accelerates the phase space sampling without the need for ideal CVs, extensive parameters fine tuning nor the use of a large number of replicas, as demonstrated by its successful applications to protein-ligand binding and protein folding benchmark systems. Our approach shows promise as a new direction in the development of enhanced sampling techniques for molecular dynamics simulations, providing an efficient and robust framework for the study of complex and unexplored problems.

First principles reaction discovery: from the Schrodinger equation to experimental prediction for methane pyrolysis

Our recent success in exploiting graphical processing units (GPUs) to accelerate quantum chemistry computations led to the development of the ab initio nanoreactor, a computational framework for automatic reaction discovery and kinetic model construction. In this work, we apply the ab initio nanoreactor to methane pyrolysis, from automatic reaction discovery to path refinement and kinetic modeling. Elementary reactions occurring during methane pyrolysis are revealed using GPU-accelerated ab initio molecular dynamics simulations. Subsequently, these reaction paths are refined at a higher level of theory with optimized reactant, product, and transition state geometries. Reaction rate coefficients are calculated by transition state theory based on the optimized reaction paths. The discovered reactions lead to a kinetic model with 53 species and 134 reactions, which is validated against experimental data and simulations using literature kinetic models. We highlight the advantage of leveraging local brute force and Monte Carlo sensitivity analysis approaches for efficient identification of important reactions. Both sensitivity approaches can further improve the accuracy of the methane pyrolysis kinetic model. The results in this work demonstrate the power of the ab initio nanoreactor framework for computationally affordable systematic reaction discovery and accurate kinetic modeling.

Improving Speed and Affordability without Compromising Accuracy: Standard Binding Free-Energy Calculations Using an Enhanced Sampling Algorithm, Multiple-Time Stepping, and Hydrogen Mass Repartitioning

Accurate evaluation of protein-ligand binding free energies in silico is of paramount importance for understanding the mechanisms of biological regulation and providing a theoretical basis for drug design and discovery. Based on a series of atomistic molecular dynamics simulations in an explicit solvent, using well-tempered metadynamics extended adaptive biasing force (WTM-eABF) as an enhanced sampling algorithm, the so-called "geometrical route" offers a rigorous theoretical framework for binding affinity calculations that match experimental values. However, although robust, this strategy remains expensive, requiring substantial computational time to achieve convergence of the simulations. Improving the efficiency of the geometrical route, while preserving its reliability through improved ergodic sampling, is, therefore, highly desirable. In this contribution, having identified the computational bottleneck of the geometrical route, to accelerate the calculations we combine (i) a longer time step for the integration of the equations of motion with hydrogen-mass repartitioning (HMR), and (ii) multiple time-stepping (MTS) for collective-variable and biasing-force evaluation. Altogether, we performed 50 independent WTM-eABF simulations in triplicate for the "physical" separation of the Abl kinase-SH3 domain:p41 complex, following different HMR and MTS schemes, while tuning, in distinct protocols, the parameters of the enhanced-sampling algorithm. To demonstrate the consistency and reliability of the results obtained with the best-performing setups, we carried out quintuple simulations. Furthermore, we demonstrated the transferability of our method to other complexes by triplicating a 200 ns separation simulation of nine chosen protocols for the MDM2-p53:NVP-CGM097 complex. [Holzer et al. J. Med. Chem. 2015, 58, 6348-6358.] Our results, based on an aggregate simulation time of 14.4 μs, allowed an optimal set of parameters to be identified, able to accelerate convergence by a factor of three without any noticeable loss of accuracy.