Semi-supervised Hierarchical Drug Embedding in Hyperbolic Space

A promising drug repurposing approach for Alzheimer's treatment: Givinostat improves cognitive behavior and pathological features in APP/PS1 mice

Alzheimer's disease (AD) is the most common neurodegenerative disease, characterized by memory loss, speech and motor defects, personality changes, and psychological disorders. The exact cause of AD remains unclear. Current treatments focus on maintaining neurotransmitter levels or targeting β-amyloid (Aβ) protein, but these only alleviate symptoms and do not reverse the disease. Developing new drugs is time-consuming, costly, and has a high failure rate. Utilizing multi-omics for drug repositioning has emerged as a new strategy. Based on transcriptomic perturbation data of over 40,000 drugs in human cells from the LINCS-L1000 database, our study employed the Jaccard index and hypergeometric distribution test for reverse transcriptional feature matching analysis, identifying Givinostat as a potential treatment for AD. Our research found that Givinostat improved cognitive behavior and brain pathology in models and enhanced hippocampal synaptic plasticity. Transcriptome sequencing revealed increased expression of mitochondrial respiratory chain complex proteins in the brains of APP/PS1 mice after Givinostat treatment. Functionally, Givinostat restored mitochondrial membrane potential, reduced reactive oxygen species, and increased ATP content in Aβ-induced HT22 cells. Additionally, it improved mitochondrial morphology and quantity in the hippocampus of APP/PS1 mice and enhanced brain glucose metabolic activity. These effects are linked to Givinostat promoting mitochondrial biogenesis and improving mitochondrial function. In summary, Givinostat offers a promising new strategy for AD treatment by targeting mitochondrial dysfunction.

Weakly supervised video anomaly detection based on hyperbolic space

In recent years, there has been a proliferation of weakly supervised methods in the field of video anomaly detection. Despite significant progress in existing research, these efforts have primarily focused on addressing this issue within Euclidean space. Conducting weakly supervised video anomaly detection in Euclidean space imposes a fundamental limitation by constraining the ability to model complex patterns due to the dimensionality constraints of the embedding space and lacking the capacity to model long-term contextual information. This inadequacy can lead to misjudgments of anomalous events due to insufficient video representation. However, hyperbolic space has shown significant potential for modeling complex data, offering new insights. In this paper, we rethink weakly supervised video anomaly detection with a novel perspective: transforming video features from Euclidean space into hyperbolic space may enable the network to learn implicit relationships in normal and anomalous videos, thereby enhancing its ability to effectively distinguish between them. Finally, to validate our approach, we conducted extensive experiments on the UCF-Crime and XD-Violence datasets. Experimental results show that our method not only has the lowest number of parameters but also achieves state-of-the-art performance on the XD-Violence dataset using only RGB information.

Towards explainable interaction prediction: Embedding biological hierarchies into hyperbolic interaction space

Given the prolonged timelines and high costs associated with traditional approaches, accelerating drug development is crucial. Computational methods, particularly drug-target interaction prediction, have emerged as efficient tools, yet the explainability of machine learning models remains a challenge. Our work aims to provide more interpretable interaction prediction models using similarity-based prediction in a latent space aligned to biological hierarchies. We investigated integrating drug and protein hierarchies into a joint-embedding drug-target latent space via embedding regularization by conducting a comparative analysis between models employing traditional flat Euclidean vector spaces and those utilizing hyperbolic embeddings. Besides, we provided a latent space analysis as an example to show how we can gain visual insights into the trained model with the help of dimensionality reduction. Our results demonstrate that hierarchy regularization improves interpretability without compromising predictive performance. Furthermore, integrating hyperbolic embeddings, coupled with regularization, enhances the quality of the embedded hierarchy trees. Our approach enables a more informed and insightful application of interaction prediction models in drug discovery by constructing an interpretable hyperbolic latent space, simultaneously incorporating drug and target hierarchies and pairing them with available interaction information. Moreover, compatible with pairwise methods, the approach allows for additional transparency through existing explainable AI solutions.

A Drug Repositioning Approach Reveals Ergotamine May Be a Potential Drug for the Treatment of Alzheimer's Disease

Background

Alzheimer's disease (AD) is a neurodegenerative disorder that is the most common form of dementia in the elderly. The drugs currently used to treat AD only have limited effects and are not able to cure the disease. Drug repositioning has increasingly become a promising approach to find potential drugs for diseases like AD.

Objective

To screen potential drug candidates for AD based on the relationship between risk genes of AD and drugs.

Methods

We collected the risk genes of AD and retrieved the information of known drugs from DrugBank. Then, the AD-related genes and the targets of each drug were mapped to the human protein-protein interaction network (PPIN) to represent AD and the drugs on the network. The network distances between each drug and AD were calculated to screen the drugs proximal to AD-related genes on PPIN, and the screened drug candidates were further analyzed by molecular docking and molecular dynamics simulations.

Results

We compiled a list of 714 genes associated with AD. From 5,833 drugs used for human diseases, we identified 1,044 drugs that could be potentially used to treat AD. Then, amyloid-β (Aβ) protein, the key molecule involved in the pathogenesis of AD was selected as the target to further screen drugs that may inhibit Aβ aggregation by molecular docking. We found that ergotamine and RAF-265 could bind stably with Aβ. In further analysis by molecular dynamics simulations, both drugs exhibited reasonable stability.

Conclusions

Our work indicated that ergotamine and RAF-265 may be potential candidates for treating AD.

PolyID: Artificial Intelligence for Discovering Performance-Advantaged and Sustainable Polymers

A necessary transformation for a sustainable economy is the transition from fossil-derived plastics to polymers derived from biomass and waste resources. While renewable feedstocks can enhance material performance through unique chemical moieties, probing the vast material design space by experiment alone is not practically feasible. Here, we develop a machine-learning-based tool, PolyID, to reduce the design space of renewable feedstocks to enable efficient discovery of performance-advantaged, biobased polymers. PolyID is a multioutput, graph neural network specifically designed to increase accuracy and to enable quantitative structure-property relationship (QSPR) analysis for polymers. It includes a novel domain-of-validity method that was developed and applied to demonstrate how gaps in training data can be filled to improve accuracy. The model was benchmarked with both a 20% held-out subset of the original training data and 22 experimentally synthesized polymers. A mean absolute error for the glass transition temperatures of 19.8 and 26.4 °C was achieved for the test and experimental data sets, respectively. Predictions were made on polymers composed of monomers from four databases that contain biologically accessible small molecules: MetaCyc, MINEs, KEGG, and BiGG. From 1.4 × 106 accessible biobased polymers, we identified five poly(ethylene terephthalate) (PET) analogues with predicted improvements to thermal and transport performance. Experimental validation for one of the PET analogues demonstrated a glass transition temperature between 85 and 112 °C, which is higher than PET and within the predicted range of the PolyID tool. In addition to accurate predictions, we show how the model's predictions are explainable through analysis of individual bond importance for a biobased nylon. Overall, PolyID can aid the biobased polymer practitioner to navigate the vast number of renewable polymers to discover sustainable materials with enhanced performance.

FLONE: fully Lorentz network embedding for inferring novel drug targets

Motivation

To predict drug targets, graph-based machine-learning methods have been widely used to capture the relationships between drug, target and disease entities in drug-disease-target (DDT) networks. However, many methods cannot explicitly consider disease types at inference time and so will predict the same target for a given drug under any disease condition. Meanwhile, DDT networks are usually organized hierarchically carrying interactive relationships between involved entities, but these methods, especially those based on Euclidean embedding cannot fully utilize such topological information, which might lead to sub-optimal results. We hypothesized that, by importing hyperbolic embedding specifically for modeling hierarchical DDT networks, graph-based algorithms could better capture relationships between aforementioned entities, which ultimately improves target prediction performance.

Results

We formulated the target prediction problem as a knowledge graph completion task explicitly considering disease types. We proposed FLONE, a hyperbolic embedding-based method based on capturing hierarchical topological information in DDT networks. The experimental results on two DDT networks showed that by introducing hyperbolic space, FLONE generates more accurate target predictions than its Euclidean counterparts, which supports our hypothesis. We also devised hyperbolic encoders to fuse external domain knowledge, to make FLONE enable handling samples corresponding to previously unseen drugs and targets for more practical scenarios.

Availability and implementation

Source code and dataset information are at: https://github.com/arantir123/DDT_triple_prediction.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Hyperbolic matrix factorization improves prediction of drug-target associations

Past research in computational systems biology has focused more on the development and applications of advanced statistical and numerical optimization techniques and much less on understanding the geometry of the biological space. By representing biological entities as points in a low dimensional Euclidean space, state-of-the-art methods for drug-target interaction (DTI) prediction implicitly assume the flat geometry of the biological space. In contrast, recent theoretical studies suggest that biological systems exhibit tree-like topology with a high degree of clustering. As a consequence, embedding a biological system in a flat space leads to distortion of distances between biological objects. Here, we present a novel matrix factorization methodology for drug-target interaction prediction that uses hyperbolic space as the latent biological space. When benchmarked against classical, Euclidean methods, hyperbolic matrix factorization exhibits superior accuracy while lowering embedding dimension by an order of magnitude. We see this as additional evidence that the hyperbolic geometry underpins large biological networks.

De novo Prediction of Cell-Drug Sensitivities Using Deep Learning-based Graph Regularized Matrix Factorization

Application of artificial intelligence (AI) in precision oncology typically involves predicting whether the cancer cells of a patient (previously unseen by AI models) will respond to any of a set of existing anticancer drugs, based on responses of previous training cell samples to those drugs. To expand the repertoire of anticancer drugs, AI has also been used to repurpose drugs that have not been tested in an anticancer setting, i.e., predicting the anticancer effects of a new drug on previously unseen cancer cells de novo. Here, we report a computational model that addresses both of the above tasks in a unified AI framework. Our model, referred to as deep learning-based graph regularized matrix factorization (DeepGRMF), integrates neural networks, graph models, and matrix-factorization techniques to utilize diverse information from drug chemical structures, their impact on cellular signaling systems, and cancer cell cellular states to predict cell response to drugs. DeepGRMF learns embeddings of drugs so that drugs sharing similar structures and mechanisms of action (MOAs) are closely related in the embedding space. Similarly, DeepGRMF also learns representation embeddings of cells such that cells sharing similar cellular states and drug responses are closely related. Evaluation of DeepGRMF and competing models on Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets show its superiority in prediction performance. Finally, we show that the model is capable of predicting effectiveness of a chemotherapy regimen on patient outcomes for the lung cancer patients in The Cancer Genome Atlas (TCGA) dataset*.

Deep Learning Applied to Ligand-Based De Novo Drug Design

In the latest years, the application of deep generative models to suggest virtual compounds is becoming a new and powerful tool in drug discovery projects. The idea behind this review is to offer an updated view on de novo design approaches based on artificial intelligent (AI) algorithms, with a particular focus on ligand-based methods. We start this review by reporting a brief overview of the most relevant de novo design approaches developed before the use of AI techniques. We then describe the nowadays most common neural network architectures employed in ligand-based de novo design, together with an up-to-date list of more than 100 deep generative models found in the literature (2017-2020). In order to show how deep generative approaches are applied into drug discovery context, we report all the now available studies in which generated compounds have been synthetized and their biological activity tested. Finally, we discuss what we envisage as beneficial future directions for further application of deep generative models in de novo drug design.