ABSTRACT
Density functional theory (DFT) is the workhorse of computational quantum chemistry. One of its main limitations is that choosing the right functional is a non-trivial task left for human experts. The choice is particularly hard for excited state calculations when using its time-dependent formulation (TD-DFT). This is due to the approximations of the method, but also because the photophysical properties of a molecule are defined by a manifold of states that all need to be properly described. This includes not only the relative energy of the states, but also capturing the correct character, order, and intensity of the transitions. In this work, we developed a neural network to recommend functionals to be used on molecules for TD-DFT calculations, by simultaneously considering all these properties for a manifold of states. This was possible by developing a scoring system to define the accuracy of an excited state's calculation against a higher-accuracy reference. The scoring system is generalizable to any level of theory; we here applied it to evaluate the performance of common functionals of different rungs against a higher accuracy method on a large set of organic molecules. The results are collected in a database that we released and made open, providing four million data points to the community for future applications. The scoring system assigns a value between zero and one hundred to each functional for each molecule, transforming the complicated task of learning photophysical properties into a simpler regression task. We used the dataset to train a graph attention neural network to predict the scores for unseen molecules. We call this oracle DELFI (Data-driven EvaLuation of Functionals by Inference), which can be used to quickly screen and predict the ranking of functionals to calculate the optical properties of organic molecules. We validated DELFI in two in silico experiments: choosing a common functional for a series of spiropyran-merocyanine isomers and a unique functional to screen a large dataset of over 50 000 organic photovoltaic molecules, for which an extensive benchmark would be unfeasible. A corresponding web application allows DELFI to be easily run and the results to be analyzed, alleviating the hurdle of choosing the right functional for TD-DFT calculations.
ABSTRACT
Women seem to have a higher vulnerability to Alzheimer's disease (AD), but the underlying mechanisms of this sex dichotomy are not well understood. Here, we first determined the influence of sex on various aspects of Alzheimer's pathology in transgenic CRND8 mice. We demonstrate that beta-amyloid (Aß) plaque burden starts to be more severe around P180 (moderate disease stage) in female transgenics when compared to males and that aging aggravates this sex-specific difference. Furthermore, we show that female transgenics suffer from higher levels of neurovascular dysfunction around P180, resulting in impaired Aß peptide clearance across the blood-brain-barrier at P360. Female transgenics show also higher levels of diffuse microgliosis and inflammation, but the density of microglial cells surrounding Aß plaques is less in females. In line with this finding, testosterone compared to estradiol was able to improve microglial viability and Aß clearance in vitro. The spatial memory of transgenics was in general poorer than in wildtypes and at P360 worse in females irrespective of their genotype. This difference was accompanied by a slightly diminished dendritic complexity in females. While all the above-named sex-differences emerged after the onset of Aß pathology, kallikrein-8 (KLK8) protease levels were, as an exception, higher in female than in male brains very early when virtually no plaques were detectable. In a second step, we quantified cerebral KLK8 levels in AD patients and healthy controls, and could ascertain, similar to mice, higher KLK8 levels not only in AD-affected but also in healthy brains of women. Accordingly, we could demonstrate that estradiol but not testosterone induces KLK8 synthesis in neuronal and microglial cells. In conclusion, multiple features of AD are more pronounced in females. Here, we show for the first time that this sex-specific difference may be meditated by estrogen-induced KLK8 overproduction long before AD pathology emerges.
