261 results found
Gainza P, Wehrle S, Van Hall-Beauvais A, et al., 2023, De novo design of protein interactions with learned surface fingerprints., Nature, Vol: 617, Pages: 176-184
Physical interactions between proteins are essential for most biological processes governing life1. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. This knowledge gap has been a major obstacle for the comprehensive understanding of cellular protein-protein interaction networks and for the de novo design of protein binders that are crucial for synthetic biology and translational applications2-9. Here we use a geometric deep-learning framework operating on protein surfaces that generates fingerprints to describe geometric and chemical features that are critical to drive protein-protein interactions10. We hypothesized that these fingerprints capture the key aspects of molecular recognition that represent a new paradigm in the computational design of novel protein interactions. As a proof of principle, we computationally designed several de novo protein binders to engage four protein targets: SARS-CoV-2 spike, PD-1, PD-L1 and CTLA-4. Several designs were experimentally optimized, whereas others were generated purely in silico, reaching nanomolar affinity with structural and mutational characterization showing highly accurate predictions. Overall, our surface-centric approach captures the physical and chemical determinants of molecular recognition, enabling an approach for the de novo design of protein interactions and, more broadly, of artificial proteins with function.
Kazi A, Cosmo L, Ahmadi S-A, et al., 2023, Differentiable Graph Module (DGM) for Graph Convolutional Networks., IEEE Trans Pattern Anal Mach Intell, Vol: 45, Pages: 1606-1617
Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful deep neural architectures to non-euclidean structured data. Such methods have shown promising results on a broad spectrum of applications ranging from social science, biomedicine, and particle physics to computer vision, graphics, and chemistry. One of the limitations of the majority of current graph neural network architectures is that they are often restricted to the transductive setting and rely on the assumption that the underlying graph is known and fixed. Often, this assumption is not true since the graph may be noisy, or partially and even completely unknown. In such cases, it would be helpful to infer the graph directly from the data, especially in inductive settings where some nodes were not present in the graph at training time. Furthermore, learning a graph may become an end in itself, as the inferred structure may provide complementary insights next to the downstream task. In this paper, we introduce Differentiable Graph Module (DGM), a learnable function that predicts edge probabilities in the graph which are optimal for the downstream task. DGM can be combined with convolutional graph neural network layers and trained in an end-to-end fashion. We provide an extensive evaluation of applications from the domains of healthcare (disease prediction), brain imaging (age prediction), computer graphics (3D point cloud segmentation), and computer vision (zero-shot learning). We show that our model provides a significant improvement over baselines both in transductive and inductive settings and achieves state-of-the-art results.
Bouritsas G, Frasca F, Zafeiriou S, et al., 2023, Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol: 45, Pages: 657-668, ISSN: 0162-8828
- Author Web Link
- Citations: 3
Rusch TK, Chamberlain BP, Mahoney MW, et al., 2022, Gradient Gating for Deep Multi-Rate Learning on Graphs
We present Gradient Gating (G$^2$), a novel framework for improving theperformance of Graph Neural Networks (GNNs). Our framework is based on gatingthe output of GNN layers with a mechanism for multi-rate flow of messagepassing information across nodes of the underlying graph. Local gradients areharnessed to further modulate message passing updates. Our framework flexiblyallows one to use any basic GNN layer as a wrapper around which the multi-rategradient gating mechanism is built. We rigorously prove that G$^2$ alleviatesthe oversmoothing problem and allows the design of deep GNNs. Empirical resultsare presented to demonstrate that the proposed framework achievesstate-of-the-art performance on a variety of graph learning tasks, including onlarge-scale heterophilic graphs.
Chamberlain BP, Shirobokov S, Rossi E, et al., 2022, Graph Neural Networks for Link Prediction with Subgraph Sketching, The Eleventh International Conference on Learning Representations 2023 (oral - top 5%)
Many Graph Neural Networks (GNNs) perform poorly compared to simpleheuristics on Link Prediction (LP) tasks. This is due to limitations inexpressive power such as the inability to count triangles (the backbone of mostLP heuristics) and because they can not distinguish automorphic nodes (thosehaving identical structural roles). Both expressiveness issues can bealleviated by learning link (rather than node) representations andincorporating structural features such as triangle counts. Since explicit linkrepresentations are often prohibitively expensive, recent works resorted tosubgraph-based methods, which have achieved state-of-the-art performance forLP, but suffer from poor efficiency due to high levels of redundancy betweensubgraphs. We analyze the components of subgraph GNN (SGNN) methods for linkprediction. Based on our analysis, we propose a novel full-graph GNN calledELPH (Efficient Link Prediction with Hashing) that passes subgraph sketches asmessages to approximate the key components of SGNNs without explicit subgraphconstruction. ELPH is provably more expressive than Message Passing GNNs(MPNNs). It outperforms existing SGNN models on many standard LP benchmarkswhile being orders of magnitude faster. However, it shares the common GNNlimitation that it is only efficient when the dataset fits in GPU memory.Accordingly, we develop a highly scalable model, called BUDDY, which usesfeature precomputation to circumvent this limitation without sacrificingpredictive performance. Our experiments show that BUDDY also outperforms SGNNson standard LP benchmarks while being highly scalable and faster than ELPH.
El-Kishky A, Bronstein M, Xiao Y, et al., 2022, Graph-based Representation Learning for Web-scale Recommender Systems, Pages: 4784-4785
Recommender systems are fundamental building blocks of modern consumer web applications that seek to predict user preferences to better serve relevant items. As such, high-quality user and item representations as inputs to recommender systems are crucial for personalized recommendation. To construct these user and item representations, self-supervised graph embedding has emerged as a principled approach to embed relational data such as user social graphs, user membership graphs, user-item engagements, and other heterogeneous graphs. In this tutorial we discuss different families of approaches to self-supervised graph embedding. Within each family, we outline a variety of techniques, their merits and disadvantages, and expound on latest works. Finally, we demonstrate how to effectively utilize the resultant large embedding tables to improve candidate retrieval and ranking in modern industry-scale deep-learning recommender systems.
Andreas J, Beguš G, Bronstein MM, et al., 2022, Toward understanding the communication in sperm whales, iScience, Vol: 25
Machine learning has been advancing dramatically over the past decade. Most strides are human-based applications due to the availability of large-scale datasets; however, opportunities are ripe to apply this technology to more deeply understand non-human communication. We detail a scientific roadmap for advancing the understanding of communication of whales that can be built further upon as a template to decipher other forms of animal and non-human communication. Sperm whales, with their highly developed neuroanatomical features, cognitive abilities, social structures, and discrete click-based encoding make for an excellent model for advanced tools that can be applied to other animals in the future. We outline the key elements required for the collection and processing of massive datasets, detecting basic communication units and language-like higher-level structures, and validating models through interactive playback experiments. The technological capabilities developed by such an undertaking hold potential for cross-applications in broader communities investigating non-human communication and behavioral research.
Cosmo L, Minello G, Bronstein M, et al., 2022, 3D Shape Analysis Through a Quantum Lens: the Average Mixing Kernel Signature, INTERNATIONAL JOURNAL OF COMPUTER VISION, Vol: 130, Pages: 1474-1493, ISSN: 0920-5691
- Author Web Link
- Citations: 3
Mahdi SS, Nauwelaers N, Joris P, et al., 2022, Matching 3D Facial Shape to Demographic Properties by Geometric Metric Learning: A Part-Based Approach., IEEE Trans Biom Behav Identity Sci, Vol: 4, Pages: 163-172
Face recognition is a widely accepted biometric identifier, as the face contains a lot of information about the identity of a person. The goal of this study is to match the 3D face of an individual to a set of demographic properties (sex, age, BMI, and genomic background) that are extracted from unidentified genetic material. We introduce a triplet loss metric learner that compresses facial shape into a lower dimensional embedding while preserving information about the property of interest. The metric learner is trained for multiple facial segments to allow a global-to-local part-based analysis of the face. To learn directly from 3D mesh data, spiral convolutions are used along with a novel mesh-sampling scheme, which retains uniformly sampled points at different resolutions. The capacity of the model for establishing identity from facial shape against a list of probe demographics is evaluated by enrolling the embeddings for all properties into a support vector machine classifier or regressor and then combining them using a naive Bayes score fuser. Results obtained by a 10-fold cross-validation for biometric verification and identification show that part-based learning significantly improves the systems performance for both encoding with our geometric metric learner or with principal component analysis.
Rusch TK, Chamberlain BP, Rowbottom J, et al., 2022, Graph-Coupled Oscillator Networks
We propose Graph-Coupled Oscillator Networks (GraphCON), a novel frameworkfor deep learning on graphs. It is based on discretizations of a second-ordersystem of ordinary differential equations (ODEs), which model a network ofnonlinear controlled and damped oscillators, coupled via the adjacencystructure of the underlying graph. The flexibility of our framework permits anybasic GNN layer (e.g. convolutional or attentional) as the coupling function,from which a multi-layer deep neural network is built up via the dynamics ofthe proposed ODEs. We relate the oversmoothing problem, commonly encountered inGNNs, to the stability of steady states of the underlying ODE and show thatzero-Dirichlet energy steady states are not stable for our proposed ODEs. Thisdemonstrates that the proposed framework mitigates the oversmoothing problem.Moreover, we prove that GraphCON mitigates the exploding and vanishinggradients problem to facilitate training of deep multi-layer GNNs. Finally, weshow that our approach offers competitive performance with respect to thestate-of-the-art on a variety of graph-based learning tasks.
Zafeiriou S, Bronstein M, Cohen T, et al., 2022, Guest Editorial: Non-Euclidean Machine Learning, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol: 44, Pages: 723-726, ISSN: 0162-8828
- Author Web Link
- Citations: 1
Cretu A-M, Monti F, Maronne S, et al., 2022, Interaction data are identifiable even across long periods of time, Nature Communications, Vol: 13, Pages: 1-11, ISSN: 2041-1723
Fine-grained records of people’s interactions, both offline and online, arecollected at large scale. These data contain sensitive information about whom wemeet, talk to, and when. We demonstrate here how people’s interaction behavioris stable over long periods of time and can be used to identify individuals inanonymous datasets. Our attack learns the profile of an individual using geometric deep learning and triplet loss optimization. In a mobile phone metadatadataset of more than 40k people, it correctly identifies 52% of individuals basedon their 2-hop interaction graph. We further show that the profiles learned byour method are stable over time and that 24% of people are still identifiableafter 20 weeks. Our results suggest that people with well-balanced interactiongraphs are more identifiable. Applying our attack to Bluetooth close-proximitynetworks, we show that even 1-hop interaction graphs are enough to identifypeople more than 26% of the time. Our results provide strong evidence thatdisconnected and even re-pseudonymized interaction data can be linked together making them personal data under the European Union’s General DataProtection Regulation.
Mahdi SS, Matthews H, Nauwelaers N, et al., 2022, Multi-Scale Part-Based Syndrome Classification of 3D Facial Images, IEEE ACCESS, Vol: 10, Pages: 23450-23462, ISSN: 2169-3536
Bevilacqua B, Frasca F, Lim D, et al., 2022, EQUIVARIANT SUBGRAPH AGGREGATION NETWORKS
Message-passing neural networks (MPNNs) are the leading architecture for deep learning on graph-structured data, in large part due to their simplicity and scalability. Unfortunately, it was shown that these architectures are limited in their expressive power. This paper proposes a novel framework called Equivariant Subgraph Aggregation Networks (ESAN) to address this issue. Our main observation is that while two graphs may not be distinguishable by an MPNN, they often contain distinguishable subgraphs. Thus, we propose to represent each graph as a set of subgraphs derived by some predefined policy, and to process it using a suitable equivariant architecture. We develop novel variants of the 1-dimensional Weisfeiler-Leman (1-WL) test for graph isomorphism, and prove lower bounds on the expressiveness of ESAN in terms of these new WL variants. We further prove that our approach increases the expressive power of both MPNNs and more expressive architectures. Moreover, we provide theoretical results that describe how design choices such as the subgraph selection policy and equivariant neural architecture affect our architecture's expressive power. To deal with the increased computational cost, we propose a subgraph sampling scheme, which can be viewed as a stochastic version of our framework. A comprehensive set of experiments on real and synthetic datasets demonstrates that our framework improves the expressive power and overall performance of popular GNN architectures.
Topping J, Di Giovanni F, Chamberlain BP, et al., 2022, UNDERSTANDING OVER-SQUASHING AND BOTTLENECKS ON GRAPHS VIA CURVATURE
Most graph neural networks (GNNs) use the message passing paradigm, in which node features are propagated on the input graph. Recent works pointed to the distortion of information flowing from distant nodes as a factor limiting the efficiency of message passing for tasks relying on long-distance interactions. This phenomenon, referred to as 'over-squashing', has been heuristically attributed to graph bottlenecks where the number of k-hop neighbors grows rapidly with k. We provide a precise description of the over-squashing phenomenon in GNNs and analyze how it arises from bottlenecks in the graph. For this purpose, we introduce a new edge-based combinatorial curvature and prove that negatively curved edges are responsible for the over-squashing issue. We also propose and experimentally test a curvature-based graph rewiring method to alleviate the over-squashing.
Gaudelet T, Day B, Jamasb AR, et al., 2021, Utilizing graph machine learning within drug discovery and development., Brief Bioinform, Vol: 22
Graph machine learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types. Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development. After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarize work incorporating: target identification, design of small molecules and biologics, and drug repurposing. Whilst the field is still emerging, key milestones including repurposed drugs entering in vivo studies, suggest GML will become a modelling framework of choice within biomedical machine learning.
Bevilacqua B, Frasca F, Lim D, et al., 2021, Equivariant Subgraph Aggregation Networks
Message-passing neural networks (MPNNs) are the leading architecture for deeplearning on graph-structured data, in large part due to their simplicity andscalability. Unfortunately, it was shown that these architectures are limitedin their expressive power. This paper proposes a novel framework calledEquivariant Subgraph Aggregation Networks (ESAN) to address this issue. Ourmain observation is that while two graphs may not be distinguishable by anMPNN, they often contain distinguishable subgraphs. Thus, we propose torepresent each graph as a set of subgraphs derived by some predefined policy,and to process it using a suitable equivariant architecture. We develop novelvariants of the 1-dimensional Weisfeiler-Leman (1-WL) test for graphisomorphism, and prove lower bounds on the expressiveness of ESAN in terms ofthese new WL variants. We further prove that our approach increases theexpressive power of both MPNNs and more expressive architectures. Moreover, weprovide theoretical results that describe how design choices such as thesubgraph selection policy and equivariant neural architecture affect ourarchitecture's expressive power. To deal with the increased computational cost,we propose a subgraph sampling scheme, which can be viewed as a stochasticversion of our framework. A comprehensive set of experiments on real andsynthetic datasets demonstrates that our framework improves the expressivepower and overall performance of popular GNN architectures.
Belli L, Tejani A, Portman F, et al., 2021, The 2021 RecSys Challenge Dataset: Fairness is not optional, Pages: 1-6
After the success the RecSys 2020 Challenge, we are describing a novel and bigger dataset that was released in conjunction with the ACM RecSys Challenge 2021. This year's dataset is not only bigger (1B data points, a 5 fold increase), but for the first time it take into consideration fairness aspects of the challenge. Unlike many static datsets, a lot of effort went into making sure that the dataset was synced with the Twitter platform: if a user deleted their content, the same content would be promptly removed from the dataset too. In this paper, we introduce the dataset and challenge, highlighting some of the issues that arise when creating recommender systems at Twitter scale.
Anelli VW, Kalloori S, Ferwerda B, et al., 2021, RecSys 2021 challenge workshop: Fairness-aware engagement prediction at scale on Twiter's Home Timeline, Pages: 819-824
The workshop features presentations of accepted contributions to the RecSys Challenge 2021, organized by Politecnico di Bari, ETH Zürich, Jönköping University, and the data set is provided by Twitter. The challenge focuses on a real-world task of tweet engagement prediction in a dynamic environment. For 2021, the challenge considers four different engagement types: Likes, Retweet, Quote, and replies. This year's challenge brings the problem even closer to Twitter's real recommender systems by introducing latency constraints. We also increases the data size to encourage novel methods. Also, the data density is increased in terms of the graph where users are considered to be nodes and interactions as edges. The goal is twofold: to predict the probability of different engagement types of a target user for a set of Tweets based on heterogeneous input data while providing fair recommendations. In fact, multi-goal optimization considering accuracy and fairness is particularly challenging. However, we believed that the recommendation community was nowadays mature enough to face the challenge of providing accurate and, at the same time, fair recommendations. To this end, Twitter has released a public dataset of close to 1 billion data points, > 40 million each day over 28 days. Week 1-3 will be used for training and week 4 for evaluation and testing. Each datapoint contains the tweet along with engagement features, user features, and tweet features. A peculiarity of this challenge is related to keeping the dataset updated with the platform: if a user deletes a Tweet, or their data from Twitter, the dataset is promptly updated. Moreover, each change in the dataset implied new evaluations of all submissions and the update of the leaderboard metrics. The challenge was well received with 578 registered users, and 386 submissions.
Nauwelaers N, Matthews H, Fan Y, et al., 2021, Exploring palatal and dental shape variation with 3D shape analysis and geometric deep learning, ORTHODONTICS & CRANIOFACIAL RESEARCH, Vol: 24, Pages: 134-143, ISSN: 1601-6335
- Author Web Link
- Citations: 6
Croquet B, Matthews H, Mertens J, et al., 2021, Automated landmarking for palatal shape analysis using geometric deep learning, ORTHODONTICS & CRANIOFACIAL RESEARCH, Vol: 24, Pages: 144-152, ISSN: 1601-6335
- Author Web Link
- Citations: 3
Bahri M, O' Sullivan E, Gong S, et al., 2021, Shape My Face: Registering 3D Face Scans by Surface-to-Surface Translation, INTERNATIONAL JOURNAL OF COMPUTER VISION, Vol: 129, Pages: 2680-2713, ISSN: 0920-5691
- Author Web Link
- Citations: 2
Chamberlain BP, Rowbottom J, Gorinova M, et al., 2021, GRAND: Graph Neural Diffusion
We present Graph Neural Diffusion (GRAND) that approaches deep learning ongraphs as a continuous diffusion process and treats Graph Neural Networks(GNNs) as discretisations of an underlying PDE. In our model, the layerstructure and topology correspond to the discretisation choices of temporal andspatial operators. Our approach allows a principled development of a broad newclass of GNNs that are able to address the common plights of graph learningmodels such as depth, oversmoothing, and bottlenecks. Key to the success of ourmodels are stability with respect to perturbations in the data and this isaddressed for both implicit and explicit discretisation schemes. We developlinear and nonlinear versions of GRAND, which achieve competitive results onmany standard graph benchmarks.
Gonzalez G, Gong S, Laponogov I, et al., 2021, Predicting anticancer hyperfoods with graph convolutional networks, Human Genomics, Vol: 15, ISSN: 1479-7364
Background:Recent efforts in the field of nutritional science have allowed the discovery of disease-beating molecules within foods based on the commonality of bioactive food molecules to FDA-approved drugs. The pioneering work in this field used an unsupervised network propagation algorithm to learn the systemic-wide effect on the human interactome of 1962 FDA-approved drugs and a supervised algorithm to predict anticancer therapeutics using the learned representations. Then, a set of bioactive molecules within foods was fed into the model, which predicted molecules with cancer-beating potential.The employed methodology consisted of disjoint unsupervised feature generation and classification tasks, which can result in sub-optimal learned drug representations with respect to the classification task. Additionally, due to the disjoint nature of the tasks, the employed approach proved cumbersome to optimize, requiring testing of thousands of hyperparameter combinations and significant computational resources.To overcome the technical limitations highlighted above, we represent each drug as a graph (human interactome) with its targets as binary node features on the graph and formulate the problem as a graph classification task. To solve this task, inspired by the success of graph neural networks in graph classification problems, we use an end-to-end graph neural network model operating directly on the graphs, which learns drug representations to optimize model performance in the prediction of anticancer therapeutics.Results:The proposed model outperforms the baseline approach in the anticancer therapeutic prediction task, achieving an F1 score of 67.99%±2.52% and an AUPR of 73.91%±3.49%. It is also shown that the model is able to capture knowledge of biological pathways to predict anticancer molecules based on the molecules’ effects on cancer-related pathways.Conclusions:We introduce an end-to-end graph convolutional model to predict cancer-beating mo
Maggioli F, Melzi S, Ovsjanikov M, et al., 2021, Orthogonalized Fourier Polynomials for Signal Approximation and Transfer, COMPUTER GRAPHICS FORUM, Vol: 40, Pages: 435-447, ISSN: 0167-7055
- Author Web Link
- Citations: 2
Andreas J, Beguš G, Bronstein MM, et al., 2021, Cetacean Translation Initiative: a roadmap to deciphering the communication of sperm whales
The past decade has witnessed a groundbreaking rise of machine learning forhuman language analysis, with current methods capable of automaticallyaccurately recovering various aspects of syntax and semantics - includingsentence structure and grounded word meaning - from large data collections.Recent research showed the promise of such tools for analyzing acousticcommunication in nonhuman species. We posit that machine learning will be thecornerstone of future collection, processing, and analysis of multimodalstreams of data in animal communication studies, including bioacoustic,behavioral, biological, and environmental data. Cetaceans are unique non-humanmodel species as they possess sophisticated acoustic communications, bututilize a very different encoding system that evolved in an aquatic rather thanterrestrial medium. Sperm whales, in particular, with their highly-developedneuroanatomical features, cognitive abilities, social structures, and discreteclick-based encoding make for an excellent starting point for advanced machinelearning tools that can be applied to other animals in the future. This paperdetails a roadmap toward this goal based on currently existing technology andmultidisciplinary scientific community effort. We outline the key elementsrequired for the collection and processing of massive bioacoustic data of spermwhales, detecting their basic communication units and language-likehigher-level structures, and validating these models through interactiveplayback experiments. The technological capabilities developed by such anundertaking are likely to yield cross-applications and advancements in broadercommunities investigating non-human communication and animal behavioralresearch.
Schonsheck SC, Bronstein MM, Lai R, 2021, Nonisometric Surface Registration via Conformal Laplace–Beltrami Basis Pursuit, Journal of Scientific Computing, Vol: 86, ISSN: 0885-7474
Surface registration is one of the most fundamental problems in geometry processing. Many approaches have been developed to tackle this problem in cases where the surfaces are nearly isometric. However, it is much more challenging to compute correspondence between surfaces which are intrinsically less similar. In this paper, we propose a variational model to align the Laplace-Beltrami (LB) eigensytems of two non-isometric genus zero shapes via conformal deformations. This method enables us to compute geometrically meaningful point-to-point maps between non-isometric shapes. Our model is based on a novel basis pursuit scheme whereby we simultaneously compute a conformal deformation of a ’target shape’ and its deformed LB eigensystem. We solve the model using a proximal alternating minimization algorithm hybridized with the augmented Lagrangian method which produces accurate correspondences given only a few landmark points. We also propose a re-initialization scheme to overcome some of the difficulties caused by the non-convexity of the variational problem. Intensive numerical experiments illustrate the effectiveness and robustness of the proposed method to handle non-isometric surfaces with large deformation with respect to both noises on the underlying manifolds and errors within the given landmarks or feature functions.
Laponogov I, Gonzalez G, Shepherd M, et al., 2021, Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19, Human Genomics, Vol: 15, Pages: 1-1, ISSN: 1479-7364
In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80-85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially "repurposed" against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a "food map" with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.
Sverrisson F, Feydy J, Correia BE, et al., 2021, Fast end-to-end learning on protein surfaces, Pages: 15267-15276, ISSN: 1063-6919
Proteins' biological functions are defined by the geometric and chemical structure of their 3D molecular surfaces. Recent works have shown that geometric deep learning can be used on mesh-based representations of proteins to identify potential functional sites, such as binding targets for potential drugs. Unfortunately though, the use of meshes as the underlying representation for protein structure has multiple drawbacks including the need to pre-compute the input features and mesh connectivities. This becomes a bottleneck for many important tasks in protein science. In this paper, we present a new framework for deep learning on protein structures that addresses these limitations. Among the key advantages of our method are the computation and sampling of the molecular surface on-the-fly from the underlying atomic point cloud and a novel efficient geometric convolutional layer. As a result, we are able to process large collections of proteins in an end-to-end fashion, taking as the sole input the raw 3D coordinates and chemical types of their atoms, eliminating the need for any hand-crafted pre-computed features. To showcase the performance of our approach, we test it on two tasks in the field of protein structural bioinformatics: the identification of interaction sites and the prediction of protein-protein interactions. On both tasks, we achieve state-of-the-art performance with much faster run times and fewer parameters than previous models. These results will considerably ease the deployment of deep learning methods in protein science and open the door for end-to-end differentiable approaches in protein modeling tasks such as function prediction and design.
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