Mapping the Space of Photoswitchable Ligands and Photodruggable Proteins with Computational Modeling.

Light-activated drugs are a promising way to localize biological activity and minimize side effects. However, their development is complicated by the numerous photophysical and biological properties that must be simultaneously optimized. To accelerate the design of photoactive drugs, we describe a procedure that combines ligand-protein docking with chemical property prediction based on machine learning (ML). We apply this procedure to 58 proteins and 9000 photo-drug candidates based on azobenzene cis-trans isomerism. We find that most proteins display a preference for trans isomers over cis and that the binding affinities of nominally active/inactive pairs are in fact highly correlated. These findings have significant value for photopharmacology research, and reinforce the need for virtual screening to identify compounds with rare desirable properties. Further, we combine our procedure with quantum chemical validation to identify promising candidates for the photoactive inhibition of PARP1, an enzyme that is over-expressed in cancer cells. The top compounds are predicted to have long-lived active forms, differential bioactivity, and absorption in the near-infrared therapeutic window.

Thermal Half-Lives of Azobenzene Derivatives: Virtual Screening Based on Intersystem Crossing Using a Machine Learning Potential.

Molecular photoswitches are the foundation of light-activated drugs. A key photoswitch is azobenzene, which exhibits trans-cis isomerism in response to light. The thermal half-life of the cis isomer is of crucial importance, since it controls the duration of the light-induced biological effect. Here we introduce a computational tool for predicting the thermal half-lives of azobenzene derivatives. Our automated approach uses a fast and accurate machine learning potential trained on quantum chemistry data. Building on well-established earlier evidence, we argue that thermal isomerization proceeds through rotation mediated by intersystem crossing, and incorporate this mechanism into our automated workflow. We use our approach to predict the thermal half-lives of 19,000 azobenzene derivatives. We explore trends and trade-offs between barriers and absorption wavelengths, and open-source our data and software to accelerate research in photopharmacology.

Excited state non-adiabatic dynamics of large photoswitchable molecules using a chemically transferable machine learning potential.

Light-induced chemical processes are ubiquitous in nature and have widespread technological applications. For example, photoisomerization can allow a drug with a photo-switchable scaffold such as azobenzene to be activated with light. In principle, photoswitches with desired photophysical properties like high isomerization quantum yields can be identified through virtual screening with reactive simulations. In practice, these simulations are rarely used for screening, since they require hundreds of trajectories and expensive quantum chemical methods to account for non-adiabatic excited state effects. Here we introduce a diabatic artificial neural network (DANN), based on diabatic states, to accelerate such simulations for azobenzene derivatives. The network is six orders of magnitude faster than the quantum chemistry method used for training. DANN is transferable to azobenzene molecules outside the training set, predicting quantum yields for unseen species that are correlated with experiment. We use the model to virtually screen 3100 hypothetical molecules, and identify novel species with high predicted quantum yields. The model predictions are confirmed using high-accuracy non-adiabatic dynamics. Our results pave the way for fast and accurate virtual screening of photoactive compounds.

GEOM, energy-annotated molecular conformations for property prediction and molecular generation.

Machine learning (ML) outperforms traditional approaches in many molecular design tasks. ML models usually predict molecular properties from a 2D chemical graph or a single 3D structure, but neither of these representations accounts for the ensemble of 3D conformers that are accessible to a molecule. Property prediction could be improved by using conformer ensembles as input, but there is no large-scale dataset that contains graphs annotated with accurate conformers and experimental data. Here we use advanced sampling and semi-empirical density functional theory (DFT) to generate 37 million molecular conformations for over 450,000 molecules. The Geometric Ensemble Of Molecules (GEOM) dataset contains conformers for 133,000 species from QM9, and 317,000 species with experimental data related to biophysics, physiology, and physical chemistry. Ensembles of 1,511 species with BACE-1 inhibition data are also labeled with high-quality DFT free energies in an implicit water solvent, and 534 ensembles are further optimized with DFT. GEOM will assist in the development of models that predict properties from conformer ensembles, and generative models that sample 3D conformations.

Bottlebrush polymers with flexible enantiomeric side chains display differential biological properties.

Chirality and molecular conformation are central components of life: biological systems rely on stereospecific interactions between discrete (macro)molecular conformers, and the impacts of stereochemistry and rigidity on the properties of small molecules and biomacromolecules have been intensively studied. Nevertheless, how these features affect the properties of synthetic macromolecules has received comparably little attention. Here we leverage iterative exponential growth and ring-opening metathesis polymerization to produce water-soluble, chiral bottlebrush polymers (CBPs) from two enantiomeric pairs of macromonomers of differing rigidity. Remarkably, CBPs with conformationally flexible, mirror image side chains show several-fold differences in cytotoxicity, cell uptake, blood pharmacokinetics and liver clearance; CBPs with comparably rigid, mirror image side chains show no differences. These observations are rationalized with a simple model that correlates greater conformational freedom with enhanced chiral recognition. Altogether, this work provides routes to the synthesis of chiral nanostructured polymers and suggests key roles for stereochemistry and conformational rigidity in the design of future biomaterials.

Synthetic Glycomacromolecules of Defined Valency, Absolute Configuration, and Topology Distinguish between Human Lectins.

Carbohydrate-binding proteins (lectins) play vital roles in cell recognition and signaling, including pathogen binding and innate immunity. Thus, targeting lectins, especially those on the surface of immune cells, could advance immunology and drug discovery. Lectins are typically oligomeric; therefore, many of the most potent ligands are multivalent. An effective strategy for lectin targeting is to display multiple copies of a single glycan epitope on a polymer backbone; however, a drawback to such multivalent ligands is they cannot distinguish between lectins that share monosaccharide binding selectivity (e.g., mannose-binding lectins) as they often lack molecular precision. Here, we describe the development of an iterative exponential growth (IEG) synthetic strategy that enables facile access to synthetic glycomacromolecules with precisely defined and tunable sizes up to 22.5 kDa, compositions, topologies, and absolute configurations. Twelve discrete mannosylated "glyco-IEGmers" are synthesized and screened for binding to a panel of mannoside-binding immune lectins (DC-SIGN, DC-SIGNR, MBL, SP-D, langerin, dectin-2, mincle, and DEC-205). In many cases, the glyco-IEGmers had distinct length, stereochemistry, and topology-dependent lectin-binding preferences. To understand these differences, we used molecular dynamics and density functional theory simulations of octameric glyco-IEGmers, which revealed dramatic effects of glyco-IEGmer stereochemistry and topology on solution structure and reveal an interplay between conformational diversity and chiral recognition in selective lectin binding. Ligand function also could be controlled by chemical substitution: by tuning the side chains of glyco-IEGmers that bind DC-SIGN, we could alter their cellular trafficking through alteration of their aggregation state. These results highlight the power of precision synthetic oligomer/polymer synthesis for selective biological targeting, motivating the development of next-generation glycomacromolecules tailored for specific immunological or other therapeutic applications.

Temperature-transferable coarse-graining of ionic liquids with dual graph convolutional neural networks.

Computer simulations can provide mechanistic insight into ionic liquids (ILs) and predict the properties of experimentally unrealized ion combinations. However, ILs suffer from a particularly large disparity in the time scales of atomistic and ensemble motion. Coarse-grained models are therefore used in place of costly all-atom simulations, accessing longer time scales and larger systems. Nevertheless, constructing the many-body potential of mean force that defines the structure and dynamics of a coarse-grained system can be complicated and computationally intensive. Machine learning shows great promise for the linked challenges of dimensionality reduction and learning the potential of mean force. To improve the coarse-graining of ILs, we present a neural network model trained on all-atom classical molecular dynamics simulations. The potential of mean force is expressed as two jointly trained neural network interatomic potentials that learn the coupled short-range and many-body long range molecular interactions. These interatomic potentials treat temperature as an explicit input variable to capture its influence on the potential of mean force. The model reproduces structural quantities with high fidelity, outperforms the temperature-independent baseline at capturing dynamics, generalizes to unseen temperatures, and incurs low simulation cost.

Multiple time scale open systems: Reaction rates and quantum coherence in model retinal photoisomerization under incoherent excitation.

Many important open quantum systems, such as light harvesting systems irradiated with natural incoherent light, present challenging computational problems. Specifically, such systems are characterized by multiple time scales over many orders of magnitude. We describe and apply an efficient approach to determine rates and dynamics in such systems. As an example, we present a theoretical and computational analysis of retinal isomerization under incoherent solar excitation using a minimal retinal model. Solar- and bath-induced Fano coherences are shown to have a small but non-negligible effect on the reaction dynamics, and the effect of Fano coherences on the reaction rate is shown to depend strongly upon the form and strength of the system-bath coupling. Using the isomerization probability to obtain the time-dependent cellular hyperpolarization, we show that the effect of coherence on hyperpolarization dynamics is small compared to the effect of natural variations in the solar intensity.

An efficient approach to the quantum dynamics and rates of processes induced by natural incoherent light.

In many important cases, the rate of excitation of a system embedded in an environment is significantly smaller than the internal system relaxation rates. An important example is that of light-induced processes under natural conditions, in which the system is excited by weak, incoherent (e.g., solar) radiation. Simulating the dynamics on the time scale of the excitation source can thus be computationally intractable. Here we describe a method for obtaining the dynamics of quantum systems without directly solving the master equation. We present an algorithm for the numerical implementation of this method and, as an example, use it to reconstruct the internal conversion dynamics of pyrazine excited by sunlight. Significantly, this approach also allows us to assess the role of quantum coherence on biological time scales, which is a topic of ongoing interest.