GraphVAMPnets for uncovering slow collective variables of self-assembly dynamics.

Uncovering slow collective variables (CVs) of self-assembly dynamics is important to elucidate its numerous kinetic assembly pathways and drive the design of novel structures for advanced materials through the bottom-up approach. However, identifying the CVs for self-assembly presents several challenges. First, self-assembly systems often consist of identical monomers, and the feature representations should be invariant to permutations and rotational symmetries. Physical coordinates, such as aggregate size, lack high-resolution detail, while common geometric coordinates like pairwise distances are hindered by the permutation and rotational symmetry challenges. Second, self-assembly is usually a downhill process, and the trajectories often suffer from insufficient sampling of backward transitions that correspond to the dissociation of self-assembled structures. Popular dimensionality reduction methods, such as time-structure independent component analysis, impose detailed balance constraints, potentially obscuring the true dynamics of self-assembly. In this work, we employ GraphVAMPnets, which combines graph neural networks with a variational approach for Markovian process (VAMP) theory to identify the slow CVs of the self-assembly processes. First, GraphVAMPnets bears the advantages of graph neural networks, in which the graph embeddings can represent self-assembly structures in high-resolution while being invariant to permutations and rotational symmetries. Second, it is built upon VAMP theory, which studies Markov processes without forcing detailed balance constraints, which addresses the out-of-equilibrium challenge in the self-assembly process. We demonstrate GraphVAMPnets for identifying slow CVs of self-assembly kinetics in two systems: the aggregation of two hydrophobic molecules and the self-assembly of patchy particles. We expect that our GraphVAMPnets can be widely applied to molecular self-assembly.

Mining of neurological adverse events associated with valbenazine: A post-marketing analysis based on FDA adverse event reporting system.

PURPOSE: Valbenazine is commonly used to treat tardive dyskinesia, and we conducted a pharmacovigilance analysis using the Food and Drug Administration Adverse Event Reporting System (FAERS) to evaluate neurological safety signals associated with valbenazine. METHODS: Data was collected in FAERS from the second quarter of 2017 to the fourth quarter of 2023 for data cleaning. Neurological adverse event (AE) signals of valbenazine were mined by calculating reporting odds ratios (ROR), information component (IC) and empirical Bayesian geometric mean (EBGM). The serious and non-serious cases and signals were prioritized using a rating scale. RESULTS: The number of neurological AE reports where the primary suspect (PS) drug was 8981 for valbenazine. Significant AE signals were identified by the preferred term (PT) analysis for valbenazine, including somnolence (ROR 19.69), tremor (ROR 15.17), and tardive dyskinesia (ROR 236.91), among which 18 AEs were identified as new signals. Patient age (p < 0.009) and sex (p = 0.197) might be associated with an increased risk of neurological AE severity. Notably, the association between valbenazine and neurological disorders remained when stratified by sex, age, and reporter type. AE timing analysis was performed for the drug and four moderate clinical priority signals [i.e., somnolence, balance disorder, parkinsonism, and akathisia (priorities 7)], showing the same early failure type profiles. CONCLUSIONS: The increase in neurological safety signals is identified in the post-marketing research of valbenazine. Clinicians need to pay attention to not only common AEs but also be alert to new neurological AE signals when using valbenazine.

An Efficient Path Classification Algorithm Based on Variational Autoencoder to Identify Metastable Path Channels for Complex Conformational Changes.

Conformational changes (i.e., dynamic transitions between pairs of conformational states) play important roles in many chemical and biological processes. Constructing the Markov state model (MSM) from extensive molecular dynamics (MD) simulations is an effective approach to dissect the mechanism of conformational changes. When combined with transition path theory (TPT), MSM can be applied to elucidate the ensemble of kinetic pathways connecting pairs of conformational states. However, the application of TPT to analyze complex conformational changes often results in a vast number of kinetic pathways with comparable fluxes. This obstacle is particularly pronounced in heterogeneous self-assembly and aggregation processes. The large number of kinetic pathways makes it challenging to comprehend the molecular mechanisms underlying conformational changes of interest. To address this challenge, we have developed a path classification algorithm named latent-space path clustering (LPC) that efficiently lumps parallel kinetic pathways into distinct metastable path channels, making them easier to comprehend. In our algorithm, MD conformations are first projected onto a low-dimensional space containing a small set of collective variables (CVs) by time-structure-based independent component analysis (tICA) with kinetic mapping. Then, MSM and TPT are constructed to obtain the ensemble of pathways, and a deep learning architecture named the variational autoencoder (VAE) is used to learn the spatial distributions of kinetic pathways in the continuous CV space. Based on the trained VAE model, the TPT-generated ensemble of kinetic pathways can be embedded into a latent space, where the classification becomes clear. We show that LPC can efficiently and accurately identify the metastable path channels in three systems: a 2D potential, the aggregation of two hydrophobic particles in water, and the folding of the Fip35 WW domain. Using the 2D potential, we further demonstrate that our LPC algorithm outperforms the previous path-lumping algorithms by making substantially fewer incorrect assignments of individual pathways to four path channels. We expect that LPC can be widely applied to identify the dominant kinetic pathways underlying complex conformational changes.