190 research outputs found
ReactionT5: a large-scale pre-trained model towards application of limited reaction data
Transformer-based deep neural networks have revolutionized the field of
molecular-related prediction tasks by treating molecules as symbolic sequences.
These models have been successfully applied in various organic chemical
applications by pretraining them with extensive compound libraries and
subsequently fine-tuning them with smaller in-house datasets for specific
tasks. However, many conventional methods primarily focus on single molecules,
with limited exploration of pretraining for reactions involving multiple
molecules. In this paper, we propose ReactionT5, a novel model that leverages
pretraining on the Open Reaction Database (ORD), a publicly available
large-scale resource. We further fine-tune this model for yield prediction and
product prediction tasks, demonstrating its impressive performance even with
limited fine-tuning data compared to traditional models. The pre-trained
ReactionT5 model is publicly accessible on the Hugging Face platform
Learning Deep Input-Output Stable Dynamics
Learning stable dynamics from observed time-series data is an essential
problem in robotics, physical modeling, and systems biology. Many of these
dynamics are represented as an inputs-output system to communicate with the
external environment. In this study, we focus on input-output stable systems,
exhibiting robustness against unexpected stimuli and noise. We propose a method
to learn nonlinear systems guaranteeing the input-output stability. Our
proposed method utilizes the differentiable projection onto the space
satisfying the Hamilton-Jacobi inequality to realize the input-output
stability. The problem of finding this projection can be formulated as a
quadratic constraint quadratic programming problem, and we derive the
particular solution analytically. Also, we apply our method to a toy bistable
model and the task of training a benchmark generated from a glucose-insulin
simulator. The results show that the nonlinear system with neural networks by
our method achieves the input-output stability, unlike naive neural networks.
Our code is available at https://github.com/clinfo/DeepIOStability.Comment: Accepted in NeurIPS 202
Computer-aided diagnosis of lung nodule using gradient tree boosting and Bayesian optimization
We aimed to evaluate computer-aided diagnosis (CADx) system for lung nodule
classification focusing on (i) usefulness of gradient tree boosting (XGBoost)
and (ii) effectiveness of parameter optimization using Bayesian optimization
(Tree Parzen Estimator, TPE) and random search. 99 lung nodules (62 lung
cancers and 37 benign lung nodules) were included from public databases of CT
images. A variant of local binary pattern was used for calculating feature
vectors. Support vector machine (SVM) or XGBoost was trained using the feature
vectors and their labels. TPE or random search was used for parameter
optimization of SVM and XGBoost. Leave-one-out cross-validation was used for
optimizing and evaluating the performance of our CADx system. Performance was
evaluated using area under the curve (AUC) of receiver operating characteristic
analysis. AUC was calculated 10 times, and its average was obtained. The best
averaged AUC of SVM and XGBoost were 0.850 and 0.896, respectively; both were
obtained using TPE. XGBoost was generally superior to SVM. Optimal parameters
for achieving high AUC were obtained with fewer numbers of trials when using
TPE, compared with random search. In conclusion, XGBoost was better than SVM
for classifying lung nodules. TPE was more efficient than random search for
parameter optimization.Comment: 29 pages, 4 figure
Tomographic Image Reconstruction Based on Minimization of Symmetrized Kullback-Leibler Divergence
Iterative reconstruction (IR) algorithms based on the principle of optimization are known for producing better reconstructed images in computed tomography. In this paper, we present an IR algorithm based on minimizing a symmetrized Kullback-Leibler divergence (SKLD) that is called Jeffreys’ J-divergence. The SKLD with iterative steps is guaranteed to decrease in convergence monotonically using a continuous dynamical method for consistent inverse problems. Specifically, we construct an autonomous differential equation for which the proposed iterative formula gives a first-order numerical discretization and demonstrate the stability of a desired solution using Lyapunov’s theorem. We describe a hybrid Euler method combined with additive and multiplicative calculus for constructing an effective and robust discretization method, thereby enabling us to obtain an approximate solution to the differential equation.We performed experiments and found that the IR algorithm derived from the hybrid discretization achieved high performance
AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge
Computer-aided synthesis planning (CASP) aims to assist chemists in performing retrosynthetic analysis for which they utilize their experiments, intuition, and knowledge. Recent breakthroughs in machine learning (ML) techniques, including deep neural networks, have significantly improved data-driven synthetic route designs without human intervention. However, learning chemical knowledge by ML for practical synthesis planning has not yet been adequately achieved and remains a challenging problem. In this study, we developed a data-driven CASP application integrated with various portions of retrosynthesis knowledge called “ReTReK” that introduces the knowledge as adjustable parameters into the evaluation of promising search directions. The experimental results showed that ReTReK successfully searched synthetic routes based on the specified retrosynthesis knowledge, indicating that the synthetic routes searched with the knowledge were preferred to those without the knowledge. The concept of integrating retrosynthesis knowledge as adjustable parameters into a data-driven CASP application is expected to enhance the performance of both existing data-driven CASP applications and those under development
Improving Compound–Protein Interaction Prediction by Self-Training with Augmenting Negative Samples
Identifying compound-protein interactions (CPIs) is crucial for drug discovery. Since experimentally validating CPIs is often time-consuming and costly, computational approaches are expected to facilitate the process. Rapid growths of available CPI databases have accelerated the development of many machine-learning methods for CPI predictions. However, their performance, particularly their generalizability against external data, often suffers from a data imbalance attributed to the lack of experimentally validated inactive (negative) samples. In this study, we developed a self-training method for augmenting both credible and informative negative samples to improve the performance of models impaired by data imbalances. The constructed model demonstrated higher performance than those constructed with other conventional methods for solving data imbalances, and the improvement was prominent for external datasets. Moreover, examination of the prediction score thresholds for pseudo-labeling during self-training revealed that augmenting the samples with ambiguous prediction scores is beneficial for constructing a model with high generalizability. The present study provides guidelines for improving CPI predictions on real-world data, thus facilitating drug discovery
GraphIX: Graph-based In silico XAI(explainable artificial intelligence) for drug repositioning from biopharmaceutical network
Drug repositioning holds great promise because it can reduce the time and
cost of new drug development. While drug repositioning can omit various R&D
processes, confirming pharmacological effects on biomolecules is essential for
application to new diseases. Biomedical explainability in a drug repositioning
model can support appropriate insights in subsequent in-depth studies. However,
the validity of the XAI methodology is still under debate, and the
effectiveness of XAI in drug repositioning prediction applications remains
unclear. In this study, we propose GraphIX, an explainable drug repositioning
framework using biological networks, and quantitatively evaluate its
explainability. GraphIX first learns the network weights and node features
using a graph neural network from known drug indication and knowledge graph
that consists of three types of nodes (but not given node type information):
disease, drug, and protein. Analysis of the post-learning features showed that
node types that were not known to the model beforehand are distinguished
through the learning process based on the graph structure. From the learned
weights and features, GraphIX then predicts the disease-drug association and
calculates the contribution values of the nodes located in the neighborhood of
the predicted disease and drug. We hypothesized that the neighboring protein
node to which the model gave a high contribution is important in understanding
the actual pharmacological effects. Quantitative evaluation of the validity of
protein nodes' contribution using a real-world database showed that the high
contribution proteins shown by GraphIX are reasonable as a mechanism of drug
action. GraphIX is a framework for evidence-based drug discovery that can
present to users new disease-drug associations and identify the protein
important for understanding its pharmacological effects from a large and
complex knowledge base.Comment: add supplementary material
Prediction and visualization of acute kidney injury in intensive care unit using one-dimensional convolutional neural networks based on routinely collected data
Background: Acute kidney injury (AKI) occurs frequently in in-hospital patients, especially in the intensive care unit (ICU), due to various etiologies including septic shock. It is clinically important to identify high-risk patients at an early stage and perform the appropriate intervention. Methods: We proposed a system to predict AKI using one-dimensional convolutional neural networks (1D-CNN) with the real-time calculation of the probability of developing AKI, along with the visualization of the rationale behind prediction using score-weighted class activation mapping and guided backpropagation. The system was applied to predicting developing AKI based on the KDIGO guideline in time windows of 24 to 48 h using data of 0 to 24 h after admission to ICU. Results: The comparison result of multiple algorithms modeling time series data indicated that the proposed 1D-CNN model achieved higher performance compared to the other models, with the mean area under the receiver operating characteristic curve of 0.742 ± 0.010 for predicting stage 1, and 0.844 ± 0.029 for stage 2 AKI using the input of the vital signs, the demographic information, and serum creatinine values. The visualization results suggested the reasonable interpretation that time points with higher respiratory rate, lower blood pressure, as well as lower SpO2, had higher attention in terms of predicting AKI, and thus important for prediction. Conclusions: We presumed the proposed system's potential usefulness as it could be applied and transferred to almost any ICU setting that stored the time series data corresponding to vital signs
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