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Abstract BackgroundCannabis sativaL. with a rich history of traditional medicinal use, has garnered significant attention in contemporary research for its potential therapeutic applications in various human diseases, including pain, inflammation, cancer, and osteoarthritis. However, the specific molecular targets and mechanisms underlying the synergistic effects of its diverse phytochemical constituents remain elusive. Understanding these mechanisms is crucial for developing targeted, effective cannabis-based therapies. MethodsTo investigate the molecular targets and pathways involved in the synergistic effects of cannabis compounds, we utilized DRIFT, a deep learning model that leverages attention-based neural networks to predict compound-target interactions. We considered both whole plant extracts and specific plant-based formulations. Predicted targets were then mapped to the Reactome pathway database to identify the biological processes affected. To facilitate the prediction of molecular targets and associated pathways for any user-specified cannabis formulation, we developed CANDI (Cannabis-derived compound Analysis and Network Discovery Interface), a web-based server. This platform offers a user-friendly interface for researchers and drug developers to explore the therapeutic potential of cannabis compounds. ResultsOur analysis using DRIFT and CANDI successfully identified numerous molecular targets of cannabis compounds, many of which are involved in pathways relevant to pain, inflammation, cancer, and other diseases. The CANDI server enables researchers to predict the molecular targets and affected pathways for any specific cannabis formulation, providing valuable insights for developing targeted therapies. ConclusionsBy combining computational approaches with knowledge of traditional cannabis use, we have developed the CANDI server, a tool that allows us to harness the therapeutic potential of cannabis compounds for the effective treatment of various disorders. By bridging traditional pharmaceutical development with cannabis-based medicine, we propose a novel approach for botanical-based treatment modalities. 

Abstract Molecular dynamics (MD) is the primary computational method by which modern structural biology explores macromolecule structure and function. Boltzmann generators have been proposed as an alternative to MD, by replacing the integration of molecular systems over time with the training of generative neural networks. This neural network approach to MD enables convergence to thermodynamic equilibrium faster than traditional MD; however, critical gaps in the theory and computational feasibility of Boltzmann generators significantly reduce their usability. Here, we develop a mathematical foundation to overcome these barriers; we demonstrate that the Boltzmann generator approach is sufficiently rapid to replace traditional MD for complex macromolecules, such as proteins in specific applications, and we provide a comprehensive toolkit for the exploration of molecular energy landscapes with neural networks. 

Abstract Protein aggregation results in an array of different size soluble oligomers and larger insoluble fibrils. Insoluble fibrils were originally thought to cause neuronal cell deaths in neurodegenerative diseases due to their prevalence in tissue samples and disease models. Despite recent studies demonstrating the toxicity associated with soluble oligomers, many therapeutic strategies still focus on fibrils or consider all types of aggregates as one group. Oligomers and fibrils require different modeling and therapeutic strategies, targeting the toxic species is crucial for successful study and therapeutic development. Here, we review the role of different‐size aggregates in disease, and how factors contributing to aggregation (mutations, metals, post‐translational modifications, and lipid interactions) may promote oligomers opposed to fibrils. We review two different computational modeling strategies (molecular dynamics and kinetic modeling) and how they are used to model both oligomers and fibrils. Finally, we outline the current therapeutic strategies targeting aggregating proteins and their strengths and weaknesses for targeting oligomers versus fibrils. Altogether, we aim to highlight the importance of distinguishing the difference between oligomers and fibrils and determining which species is toxic when modeling and creating therapeutics for protein aggregation in disease. 

In the evolving field of quantum computing, optimizing Quantum Error Correction (QEC) parameters is crucial due to the varying types and amounts of physical noise across quantum computers. Traditional simulators use a forward paradigm to derive logical error rates from inputs like code distance and rounds, but this can lead to resource wastage. Adjusting QEC parameters manually with tools like STIM is often inefficient, especially given the daily fluctuations in quantum error rates. To address this, we introduce MITS, a reverse engineering tool for STIM that automatically determines optimal QEC settings based on a given quantum computer’s noise model and a target logical error rate. This approach minimizes qubit and gate usage by precisely matching the necessary logical error rate with the constraints of qubit numbers and gate fidelity. Our investigations into various heuristics and machine learning models for MITS show that XGBoost and Random Forest regressions, with Pearson correlation coefficients of 0.98 and 0.96, respectively, are highly effective in this context. 

Quantum error correction codes (QECCs) are essential for reliable quantum computing as they protect quantum states against noise and errors. Limited research has explored the resilience of QECCs to biased noise, critical for selecting optimal codes. We examine how different noise types impact QECCs, considering the varying susceptibility of quantum systems to specific errors. Our goal is to identify opportunities to minimize the resources—or overhead—needed for effective error correction. We conduct a detailed study on two QECCs—rotated and unrotated surface codes—under various noise models using simulations. Rotated surface codes generally perform better due to their simplicity and lower qubit overhead. They exceed the noise threshold of current quantum processors, making them more effective at lower error rates. This study highlights a hierarchy in surface code implementation based on resource demand, consistently observed across both code types. Our analysis ranks the code-capacity model as the most pessimistic and the circuit-level model as the most realistic, mapping error thresholds that show surface code advantages. Additionally, higher code distances improve performance without excessively increasing qubit overhead. Tailoring surface codes to align with the target logical error rate and the biased physical error profile is crucial for optimizing reliability and resource use.